Integrating Causal Reasoning and Reinforcement Learning for Enhanced Cybersecurity Decision-Making

Introduction

Cybersecurity systems increasingly rely on machine learning (ML) for anomaly detection and threat mitigation, yet current ML-based anomaly detection techniques face significant challenges. Organizations struggle with a flood of false positive alerts that lead to “alert fatigue,” where analysts are overwhelmed by benign anomalies . Many anomaly detection models operate as opaque black boxes, offering little interpretability into why a given alert was triggered, which erodes trust and hinders effective incident response. These systems often cannot incorporate crucial domain knowledge or context, treating all anomalies equally without understanding business or network context. Moreover, traditional detection models tend to be static – once trained, they are slow to adapt to evolving attack tactics or concept drift in data, making them brittle against new or adaptive threats.

The combination of high false positives, lack of explainability, inability to leverage expert knowledge, and rigidity of static models means that purely ML-driven security monitoring can generate noise and uncertainty instead of actionable insight. This weak link in the cyber defense chain leaves security teams reactive and overwhelmed, highlighting the need for more intelligent, adaptive, and explainable decision-making systems.

In this article, we explore how causal reasoning and reinforcement learning (RL) – two promising approaches from artificial intelligence – can be combined to overcome these limitations and improve automated decision-making and incident response in cybersecurity.

Key Terms

To ground the discussion, we first define several key terms in the context of cybersecurity and AI:
• Anomaly Detection: In cybersecurity, anomaly detection refers to identifying patterns or events in data that do not conform to expected normal behavior. An anomaly may indicate a security threat (e.g. an intrusion or malware activity) or just a benign irregularity. Techniques range from simple statistical thresholds to complex ML models that learn a baseline of “normal” system behavior and flag deviations . The challenge is to detect novel or stealthy attacks while minimizing false positives from innocuous deviations.
• Reinforcement Learning (RL): RL is a machine learning paradigm where an agent learns to make sequences of decisions by interacting with an environment and receiving feedback in the form of rewards or penalties. In the cybersecurity context, an RL agent could observe the state of a network or system (e.g. alerts, system metrics), take an action (such as blocking traffic, isolating a host, or launching an analysis task), and receive a reward signal based on the effectiveness of that action (e.g. thwarting an attack or incurring a minimal disruption). Over time, the agent learns an optimal policy for choosing actions to maximize cumulative reward (for example, maximizing security while minimizing impact on operations). Unlike static rule-based systems, RL enables adaptive decision-making through trial-and-error, allowing the agent to improve its responses as threats evolve .
• Causal Inference/Reasoning: Causal reasoning in AI is the process of modeling and inferring cause-and-effect relationships rather than just correlations. In contrast to traditional statistical learning that might say “event A is correlated with event B,” causal inference seeks to determine if “A causes B” and what happens to B if we actively intervene on A. This often involves structural causal models (SCMs) or causal graphs – directed graphs where nodes represent variables (e.g. specific system metrics or events) and edges represent causal influences . Through tools like Pearl’s do-calculus (i.e. reasoning about outcomes under hypothetical interventions), causal inference allows us to predict the effect of actions (e.g. “if we block port X, will it stop the malware or cause other issues?”) and to distinguish true causes of anomalies from spurious correlations . In cybersecurity, causal reasoning can be applied to trace the root cause of an alert (was a spike in traffic caused by benign maintenance activity or by a data exfiltration attack?) and to evaluate potential response actions via “what-if” analyses before deploying them.
• Decision-Making Systems: In this context, decision-making systems refer to automated or semi-automated systems that analyze inputs (such as security alerts or sensor data) and make decisions on mitigative or corrective actions without constant human guidance. This includes intrusion prevention systems that decide to block or allow traffic, automated incident response platforms that trigger containment scripts, or any AI system that selects among different security actions. Effective decision-making systems in cybersecurity must handle uncertainty, weigh trade-offs (e.g. security vs. availability), and execute responses that neutralize threats while minimizing negative impact. The quality of such a system is measured by the quality of decisions (accuracy, efficiency, safety) it makes in varied scenarios. Integrating advanced AI (like causal reasoning and RL) into decision-making aims to improve these choices by making them more informed, context-aware, and adaptive.
• Incident Response: Incident response is the structured process by which organizations handle and manage the aftermath of a security incident (such as a breach, malware outbreak, or policy violation) to limit damage and reduce recovery time and costs. According to standard frameworks like NIST, incident response involves phases including Preparation, Detection and Analysis, Containment, Eradication, and Recovery, and Post-Incident Activity. In practice, this means once an intrusion or anomaly is detected, responders (human or automated) analyze the situation, decide on actions (e.g. isolating affected systems, removing malware, applying patches), carry out those actions to contain the threat and restore normal operations, and later study the incident to improve future response. Automated incident response refers to the use of software and algorithms to perform some of these steps autonomously or with minimal human intervention. The challenge is making sure automated actions are accurate (responding to true incidents, not false alarms) and appropriate (mitigating the threat without excessive collateral damage). An ideal automated incident response system would dynamically choose the right countermeasures for the specific incident, essentially acting like a skilled analyst but at machine speed.

Current ML-Based Techniques for Anomaly Detection and Incident Response

ML-Based Anomaly Detection in Cybersecurity

Modern cybersecurity deployments often include ML-based anomaly detection systems to flag deviations that could signify attacks. A variety of techniques are used: statistical models (e.g. Gaussian models or PCA for outlier detection), machine learning algorithms like one-class SVMs, isolation forests, clustering methods, and more recently, deep learning approaches (autoencoders, LSTM sequence models) that learn complex patterns of normal behavior.

These anomaly detectors have notable strengths: they can potentially detect previously unseen attack patterns (zero-days) that do not match any known signature, and they can monitor high-dimensional data streams (network flows, system calls, user behaviors) to identify subtle anomalies. For example, an autoencoder might learn to reconstruct normal traffic and raise an alert when reconstruction error for a new traffic pattern is high (indicating abnormality).
However, these approaches also have significant gaps and limitations. A chief issue is the high false positive rate – many anomalies detected by ML turn out not to be security incidents (e.g. a spike in traffic might be due to a backup job rather than a DDoS attack). Ahmed et al. (2016) observe that anomaly-based intrusion detection systems can suffer from very high false alarm rates, as unusual but benign activities are often misclassified as malicious . This leads to wasted effort and ignored alerts (analysts become desensitized due to frequent false alarms ). Another limitation is the lack of interpretability of complex ML models. Security operators often get an alert with a score or label but no explanation; most anomaly detectors cannot explain why a data point was deemed anomalous or what factors contributed to that decision. This “black-box” nature means analysts have little insight into the root cause of the anomaly and whether it truly indicates an attack or a glitch. Domain knowledge – patterns that an experienced security engineer might recognize as harmless (or dangerous) – is usually not explicitly incorporated into these models, which learn purely from data. As a result, ML detectors can miss context: “lack of context” was identified as a failure mode for traditional models, which may flag outliers but cannot determine their significance or provide rationale .

Another shortcoming is that anomaly detectors alone do not differentiate between malicious anomalies and benign ones – an anomaly is not necessarily an incident. For example, an administrator performing unscheduled maintenance could trigger anomaly alerts similar to those of an attacker performing reconnaissance. Pure ML detectors typically lack the higher-level reasoning to distinguish these. Consequently, anomaly detection is often just the first step, and human analysts must investigate each alert to confirm if it’s a true security incident. This limits the automation of incident response, as the system itself cannot decide how to react to an anomaly (other than perhaps raising an alarm). In summary, while ML-based anomaly detection has improved the ability to catch novel threats, its effectiveness is hampered by false positives, interpretability issues, inability to use expert knowledge, and static behavior.

These gaps motivate augmenting anomaly detection with causal reasoning (for better understanding anomalies) and with adaptive learning (to reduce false positives and adapt to change).

Automated Incident Response: Strengths and Gaps

Automated incident response systems aim to take action when a threat is detected, without waiting for human intervention. In practice, many security tools have built-in automated responses triggered by certain events – for instance, an intrusion prevention system (IPS) might automatically block an IP address when it sees signatures of a known attack, or an endpoint security agent might quarantine a file that matches malware signatures. These rule-based automations are effective for known threats with well-defined indicators. They operate on a simple if-then logic: if a known bad pattern is seen, then execute the pre-programmed response. The strength of this approach is speed (immediate action) and consistency in handling routine events. But when it comes to more complex or novel incidents, purely rule-based automation falls short. Static playbooks cannot cover the enormous variety of possible attack scenarios and often lack the flexibility to make fine-tuned decisions in unfamiliar situations. For example, responding to a ransomware outbreak vs. a data exfiltration might require different strategies, and within each, the optimal action might depend on context (which servers are affected? what is the business value of the assets at risk?). Hard-coding all those decision rules is impractical.

This is where research has turned to AI approaches like reinforcement learning to enable more adaptive and context-aware incident response. An RL-based incident response system can learn from experience which actions are effective in neutralizing attacks and minimizing damage. For instance, an RL agent could learn a policy for an enterprise network where it decides when to disconnect a machine, when to throttle traffic, or when to deploy a patch, based on the state of the system and progression of an attack. Over time and with training (potentially in simulation environments), the agent develops an optimized strategy that balances security and other objectives (like uptime).

Existing literature has started to explore such approaches. Sequential decision-making formulations like Markov Decision Processes (MDPs) have been used to model the interaction between attackers and defenders, enabling the use of RL to derive optimal response policies. Deep reinforcement learning (DRL) algorithms (e.g. Q-learning, Deep Q Networks, or policy gradient methods like PPO) have been applied to scenarios such as network intrusion response and moving-target defense. These studies demonstrate a key strength: RL agents can, in principle, learn to handle unforeseen situations by generalizing from training experiences, rather than relying on predefined rules. They also can explicitly optimize for multiple goals via reward design – for example, a reward function might penalize both successful attacks and unnecessary service disruptions, thus pushing the agent to find a balanced response strategy.
That said, purely learned incident response agents also face gaps that have limited their real-world deployment so far. Training an RL agent for cybersecurity is challenging: the agent needs to explore different actions (some of which could be dangerous in a real network) and experience enough attack scenarios to learn effectively. Much of this training must happen in simulated environments (cyber ranges) to avoid harming production systems during learning. Even then, simulation fidelity and representing the vast space of possible attacks is difficult.

Another challenge is state representation – the agent must infer the state of the security incident from sensor data. If the observations (alerts, logs) are ambiguous, the agent might make mistakes. Importantly, a naive RL agent, like a naive human responder, could take incorrect actions that worsen the situation. One notorious issue is the potential for an automated response to cause unintended side effects; for example, an agent that automatically shuts down a suspected compromised system might inadvertently cut off a critical service. In fact, incidents have cross-domain implications: a response that mitigates a cyber threat could impact safety or operations. A vivid example is an autonomous vehicle under cyber-attack: an RL agent on the vehicle might decide to reboot a component to stop a detected anomaly, but if that component controls braking, this could cause an accident . This underscores a need for strategic decision-making that understands cause and effect – exactly where causal reasoning can help.

Causal Reasoning for Root Cause Analysis and Impact Understanding

Causal reasoning offers powerful tools to tackle two of the most vexing problems in cybersecurity analytics: identifying the root causes of anomalies and predicting the impacts of potential actions. Rather than treating the system as a black box of correlated events, a causal approach explicitly models the relationships between different variables or events, enabling the system to reason why something happened and what might happen next or if we take a certain action. Below, we explore how causal reasoning addresses challenges of existing approaches:

1. Distinguishing Causation from Correlation (Reducing False Positives):

A core issue with ML detectors is that they often latch onto correlative features that may not be truly indicative of an attack. Causal reasoning can help filter out spurious correlations by focusing on causal mechanisms. For example, a traditional anomaly detector might notice that during DDoS attacks in the past, a certain router’s CPU usage was high, and start flagging high CPU usage as an anomaly. But high CPU could also be caused by legitimate heavy workload. A causal analysis would attempt to discern whether the high CPU is caused by malicious traffic or by benign events. Zeng et al. (2022) illustrate this in their DDoS detection framework: they found that existing ML models were diagnosing “causality” between traffic features and attacks based on associative patterns, leading to false associations . By using interventions (the do-operator in Pearl’s causal inference framework) on their data, they identified and removed “noise features” that were correlated with attacks but not causal . This causal feature selection and counterfactual analysis dramatically improved detection accuracy, reducing misclassifications by filtering out misleading signals . In practical terms, incorporating causal reasoning means the system doesn’t blindly trust every anomaly indicator; it cross-examines whether that indicator could be explained by known benign causes. If an anomaly can be causally attributed to a non-security event, the system can either avoid raising an alert or at least down-weight its severity. This directly cuts down on false positives and alert fatigue.
Moreover, causal models can integrate domain knowledge in the form of known cause-effect relationships, improving detection quality. For instance, experts might assert a causal graph where “system patching activity” -> “increased CPU and network load” -> “temporary performance drop.” If an anomaly detector sees performance drop and network load, a causal reasoning layer can recognize the pattern of a patch operation (as opposed to an attack) and either explain it or exclude it from alerts. Such knowledge-driven causal rules are a way to inject context that pure data-driven methods miss. In essence, causal reasoning adds a layer of explanation to anomaly detection: rather than just noting “something is odd,” it asks “what likely caused this odd behavior?” – a critical question to avoid knee-jerk responses to mere symptoms.

2. Root Cause Analysis and Explainability:

When a security anomaly is detected, one of the first questions an analyst asks is “what is the root cause?” Was the spike in traffic triggered by an attacker, a user error, or a system malfunction? Traditional anomaly detectors usually can’t answer this – they only indicate an outlier. Causal reasoning techniques, however, are purpose-built for root cause analysis. By constructing a causal graph of the system (which might include nodes for user actions, system states, network conditions, etc.), we can trace which factors most likely caused the observed anomaly. This is often done via counterfactual reasoning: e.g., “if factor X had not occurred, would the anomaly still have happened?” If the answer is no, X is a strong candidate for root cause. Applying this to cybersecurity, suppose an anomaly detection system flags unusual outbound traffic from a server. A causal model might reveal that this server’s abnormal behavior was triggered by a recently installed program that opened a backdoor. By performing counterfactual queries (e.g., remove the presence of that program and see if traffic would normalize), the system can conclude that the new program installation caused the traffic spike. This level of diagnosis is far beyond current black-box ML alerts, and it immensely aids incident responders: knowing the root cause (in this case, a malicious program) focuses the response (remove that program, check how it got there, etc.), whereas without causal insight, responders might be guessing among many potential causes.
Causal reasoning thus provides interpretability and explanations for anomalies. Instead of an inscrutable anomaly score, the system might output a causal story: “We observe high data transfer (effect); the likely cause is process X launching numerous connections, which is unusual given the typical workload. This behavior is consistent with data exfiltration.” Such an explanation makes the alert actionable. It also increases trust in automated systems – if an AI can explain why it’s sounding an alarm (and that explanation makes sense), security teams are more likely to accept automated or autonomous responses. Industry is recognizing this need: for example, a recent anomaly detection approach by Howso leverages “Causal AI” specifically to yield interpretable results, ensuring users “understand root causes” of anomalies . This exemplifies how causal reasoning can turn a nebulous anomaly alert into a concrete narrative of cause and effect.

3. Modeling Attack Chains and Anticipating Adversary Actions:

Attackers often perform a sequence of steps (reconnaissance, exploitation, lateral movement, etc.). If we can causally link these steps, detecting one step can allow us to anticipate the next. Causal graphs can represent known attack patterns: for instance, in the MITRE ATT&CK framework of adversary techniques, one could draw causal links like “Phishing email leads to Initial Access; Initial Access enables Privilege Escalation; Privilege Escalation causes Lateral Movement; … leading to Impact” . By learning or encoding such a causal structure, a defense system that sees evidence of, say, Privilege Escalation can infer that the attacker is likely to attempt Lateral Movement next. Dhir et al. (2021) argue that if a causal structure of an attack can be learned, recognizing part of the pattern allows the defender to “amply anticipate the attacker’s next move” . This is a game-changer: instead of just reacting to isolated alerts, the system uses causal knowledge to predict how the incident might evolve. In practice, this could translate to proactively activating certain defenses (e.g., monitoring or blocking likely next targets) once earlier stages of an attack are detected.
This kind of reasoning also helps mitigate the common issue where individual events might not trigger high alerts, but their combination is dangerous. Causally linking events over time (temporal causality) means that a series of low-level anomalies can be understood as part of a single cause (an attack campaign), reducing the chance that an attacker slips through by staying low and slow. Essentially, causal reasoning can elevate detection from the event level to the scenario level – identifying an ongoing incident as a coherent chain of causes and effects.

4. Understanding the Impact of Actions (Decision Support):

Perhaps one of the most valuable aspects of causal reasoning in automated incident response is the ability to predict the consequences of different response actions. Every defensive action (blocking an IP, shutting down a server, installing a patch, etc.) is an intervention on the system. A causal model of the environment lets us ask: “What will happen if we do X?” For example, if we have a causal model that includes nodes for “System Online” -> “Business Transactions Processed”, and we consider an action “Isolate Server A” (which sets “System Online” = false for that server), the causal model might show that this will cause a drop in business transactions (a negative consequence) but also stop data exfiltration (a positive consequence). A sophisticated system could even simulate multiple candidate actions via the causal model – a kind of counterfactual simulation – to see which action achieves the best trade-off. This is akin to having a mental model of the IT environment where one can do “what-if” experiments rapidly. Without causal modeling, an automated system might take an action without foresight, potentially causing as much harm as the threat it’s mitigating.

A concrete example comes from the domain of cyber-physical systems: an autonomous vehicle experiencing a cyber attack signal. One possible response is to reboot or shut down a component that seems compromised. However, causal understanding is crucial: as noted earlier, shutting down the wrong component could cause the vehicle to malfunction (e.g., disabling a critical sensor or actuator) . A causal model of the vehicle’s control system would capture that turning off the computing unit controlling the blinking diode (from the side-channel example) would also deactivate other vital functions, potentially causing a crash . Therefore, a causal-aware responder would avoid that action and seek an alternative (maybe isolate the component’s network access instead of a full shutdown). This kind of reasoning – weighing side effects and indirect impacts – is essential in environments where security intersects with safety or business continuity.

In enterprise IT networks, similar principles apply. For instance, causal reasoning can help answer: if we block all traffic to a certain subnet to stop an intrusion, what critical services in that subnet will be impacted? Security orchestration platforms could maintain causal maps of dependencies (e.g., Service A depends on Server B; shutting Server B will cause outage to Service A). With that, an automated system can either choose a less disruptive response or at least warn administrators of the likely impact. Essentially, causal models act as the “brain” that foresees outcomes of actions, which is something purely reactive systems lack.

5. Integration with Human Knowledge and Interpretability:

Causal models can be built or augmented with expert knowledge. This means security practitioners can encode their understanding of the network and threat landscape into the AI system (for example, specifying that “if firewall logs show port scanning followed by an unusual admin login, it likely means a breach”). Unlike end-to-end ML which might discover correlations that humans don’t understand, causal rules and graphs are often more transparent. This makes it easier to verify and update the system’s logic. If an expert notices a mistake in the causal graph, they can adjust it (similar to tuning a Bayesian network) – this is much more interpretable than tweaking a neural network’s weights. The end result is a system whose decisions can be explained in terms familiar to humans (e.g., “we did X because it prevented Y which causes Z”), facilitating a meaningful human-AI collaboration in incident response.
In summary, causal reasoning addresses key challenges by providing context and understanding. It tells us why an anomaly is happening (reducing false positives and zooming in on root causes) and what might happen if we take certain actions (allowing informed, safe decision-making). By embedding causal inference into cybersecurity systems, we shift from superficial anomaly monitoring to a deeper diagnostic and prognostic capability. This lays a strong foundation upon which adaptive decision-making (via RL) can then be built, as we discuss next.

Reinforcement Learning for Adaptive, Context-Aware Responses

Reinforcement learning brings a complementary set of capabilities to cybersecurity defense: the ability for a system to learn from experience how to optimally respond to threats in a changing environment. Unlike static rule sets, an RL-based system can improve its decision-making policy over time, adapting to new attacks or system changes, much like a human analyst learning on the job. Here we discuss the role of RL in enabling adaptive, context-aware responses to anomalies and incidents, and how it addresses some limitations of static approaches:

1. Trial-and-Error Learning to Optimize Policies:

At its core, reinforcement learning is about an agent discovering which actions yield the best outcomes through feedback. In cybersecurity, defining “best outcome” can be complex – it might involve stopping attacks, minimizing downtime, preserving data integrity, etc. But we can craft a reward function that captures these goals (for example, a high penalty for successful attacks, a smaller penalty for disrupting a service, a positive reward for each time step the system remains secure). An RL agent then has the objective of maximizing cumulative reward, implicitly learning a policy that balances trade-offs. The key advantage is that the agent can explore different strategies in simulation or controlled environments, including non-obvious ones that humans might not think of. Over many episodes of simulated cyber attacks and defenses, the agent might learn tactics like selectively isolating parts of the network, setting traps (honeypots) for attackers, or dynamically changing configurations to confuse the adversary – all based on what proved effective in the training runs.
Because RL does not require an explicit model of how actions lead to outcomes (in model-free RL), it can learn from raw experience even when the environment is very complex. For example, a defender agent could learn how to respond to a multi-stage attack by experiencing many attack scenarios, without being explicitly programmed for each stage. It might learn that when certain alerts (states) occur in sequence, taking a specific action at a particular point drastically reduces the chance of attack success, thereby incorporating that into its policy. This experience-driven optimization is something static rule systems lack; they rely on foresight of programmers. RL, in contrast, can surprise us with novel defensive maneuvers that emerge from the learning process.

2. Adaptation to Evolving Threats:

One of the biggest appeals of RL in cybersecurity is its adaptability. Attackers constantly evolve – they may alter their tactics to evade known defenses. A static defense mechanism might be bypassed after an attacker learns its behavior. But an RL agent, especially if it continues to learn (online learning or periodic retraining), can adapt its strategy in response. For instance, if attackers begin using a new type of malware, the RL agent might at first fail to handle it, but as it encounters it and receives negative reward (for letting the attack succeed), it will adjust its policy to mitigate that attack in the future. This is akin to having a self-improving Blue Team. Research has noted that RL is well-suited for “dynamic and unpredictable cyber landscapes” exactly for this reason – it doesn’t need an exhaustive list of attack signatures; it learns to react appropriately even to unforeseen situations by generalizing from what it has seen. In practice, this could manifest as an intrusion response system that remains effective even as the threat landscape shifts, reducing the need for constant manual updates by security engineers.

3. Context-Aware Decision Making:

RL agents make decisions based on the state of the environment. In cybersecurity, this state can include a wealth of context: the type of anomaly detected, the system’s current condition (CPU load, network traffic patterns), time of day, criticality of assets under threat, etc. By observing these variables, an RL agent can learn to take context-dependent actions. For example, the agent might learn a policy where if a port scan is detected on a critical database server (context: high-value asset) it immediately deploys a stricter firewall rule, but if a similar port scan is on a low-priority guest network, it simply logs it. This nuanced response – effectively risk-based response – is something that would require many conditional rules to implement manually, but an RL agent can naturally develop it since the different contexts lead to different optimal actions. The result is fewer overreactions (e.g. shutting down everything for a minor threat) and fewer underreactions (e.g. ignoring signs of a serious attack). The agent’s policy encapsulates what defenders often strive for: proportional responses that depend on the situation.

4. Sequential Decision Optimization:

Incidents often involve a sequence of decisions, not just one. Take a ransomware infection scenario: a defender might first isolate the machine, then attempt to kill the ransomware process, then restore from backup, etc. Each step’s outcome influences the next. RL is inherently suited to such sequential decision problems where the goal is to optimize the long-term outcome, not just immediate effect. A defender agent using RL could weigh the benefit of immediate containment versus the risk of the malware spreading if it delays action. It might also plan multi-step countermeasures, like first diverting the attacker (perhaps by feeding it false data or shifting them to a honeypot) and then neutralizing the threat. This is a planning aspect that simple reactive systems lack. By simulating many attack/response sequences, the RL agent effectively does automated planning under uncertainty, learning what sequences of actions work best to end the incident with minimal damage.
Moreover, RL can handle partial observability to an extent (especially with approaches like POMDPs or recurrent policies), meaning even if the agent doesn’t have perfect information about the attacker’s state, it can still learn a robust policy. This is important because in real incidents we rarely know everything – there may be hidden attacker presence. An RL agent can be trained to operate under such uncertainty by optimizing worst-case or average-case outcomes via its reward structure.

5. Examples of RL in Cybersecurity Research:

Recent research and experiments underscore RL’s potential. For instance, deep RL algorithms like Proximal Policy Optimization (PPO) have been applied to network intrusion environments, where they learned to both detect and respond to threats in real-time . PPO is attractive for its stability and ability to handle high-dimensional inputs, which is useful when analyzing network traffic or host telemetry data . Studies have shown PPO-based agents capable of adapting to new attack patterns without human intervention . Other work has looked at multi-objective RL, where the agent balances objectives like maximizing security while minimizing resource usage or response costs . These multi-objective agents can find sweet spots that human tuners might miss – for example, slightly increasing response time might dramatically decrease false positives, an insight an agent could learn on its own.
One practical case demonstrated by an RL approach was an agent learning to handle malware on endpoints: the agent could choose actions like “kill process,” “delete file,” or “do nothing,” and it received reward based on whether the malware was successfully removed and how much the system was impacted. Over many trials, the agent learned to effectively eradicate malware while minimizing disruption, essentially automating what an incident responder would do but faster. Other examples include network flow control – agents learning to throttle or reroute traffic during a DDoS attack in a way that maintains service for legitimate users while starving the attack.

6. Challenges and the Need for Causal Integration:

While RL brings these adaptive strengths, it’s worth noting its challenges align with where causal reasoning can assist. RL agents are notorious for being data-hungry – they might require a huge number of training episodes to learn good policies. In a security context, generating rich training data (simulating many attacks) is non-trivial. This is where integrating a model of the environment can help (so-called model-based RL), and a causal model is an excellent candidate for that. Additionally, RL on its own doesn’t guarantee safety; an agent might stumble upon a solution that optimizes reward but is undesirable (for example, it might learn to simply shut everything down to prevent attacks, which secures the system but halts operations!). We need mechanisms to prevent such pathological strategies. Again, causal knowledge can impose structure and guardrails – for instance, penalizing actions known to cause certain bad outcomes, or using a causal simulator to test policy changes offline before deploying them.

In summary, reinforcement learning provides a framework for autonomous, adaptive decision-making in cybersecurity. It excels at improving itself with experience, handling complex sequential scenarios, and optimizing in high-dimensional, dynamic situations where traditional static rules falter . By learning from trial-and-error, RL agents can become highly attuned to the cyber environment they protect, potentially outperforming manually coded strategies. However, to unlock RL’s full potential safely and efficiently in cybersecurity, it should be combined with the kind of insight and knowledge that causal reasoning offers. This synergy is the focus of the next section, where we propose an integrated causal-RL architecture for cyber defense.

Integrated Framework: Causal Reasoning meets Reinforcement Learning in Cybersecurity

Having examined the benefits of causal reasoning and reinforcement learning separately, we now propose a conceptual framework that integrates these approaches for improved automated decision-making in cybersecurity. The goal of this integrated framework is to create a decision-making system that is both intelligent (learning optimal actions through RL) and informed (grounded in causal understanding of the environment). By combining Structural Causal Models (SCMs) with an RL agent, the system can leverage causal knowledge for better learning and decision planning, while the RL component can adjust and refine strategies over time.

Below, we outline the architecture and key components of this framework, and how they interact:

1. Causal Knowledge Base (Structural Causal Model):

At the heart of the framework lies a causal graph or SCM representing the cybersecurity environment. This graph encodes variables such as system metrics (CPU load, network throughput), security events (alerts, login failures), threat indicators (malware presence, exploit success), and potential actions (like “block IP” or “isolate host”) as nodes. Directed edges in the graph capture cause-effect relations, which can come from domain knowledge or be learned from data. For example, the SCM might include relationships like “malware infection causes high disk usage and outbound traffic”, “isolating a host causes loss of connectivity for that host’s services”, or “enabling multi-factor authentication reduces probability of account compromise”. Some parts of the causal model may be provided by experts (e.g., known IT dependencies or attack step relations from MITRE ATT&CK), while others could be learned by observing data (using causal discovery algorithms on historical incident logs). The SCM thus serves as a world model for the decision-making system – a sandbox in which we can predict the effects of actions (interventions) and reason about the likely causes of observed events.

2. Anomaly Detection and Situation Assessment Module:

Upstream of decision-making, the framework includes sensors and detectors (which could be ML-based anomaly detection systems, traditional IDS, etc.) that monitor the environment and produce alerts or observations. These observations feed into a Situation Assessment component, which uses the causal model to interpret what is happening. For instance, if an anomaly detector flags unusual traffic, the causal model can be consulted to see what likely cause could explain it (perhaps linking it to a known attack pattern). The outcome of this assessment is a state representation that goes into the RL decision module. Crucially, this state is not just raw alerts; it can be enriched with causal inferences – e.g., “Node A likely compromised via technique X; potential lateral movement in progress.” In other words, the state given to the RL agent is contextualized and interpreted information about the security incident, not just a binary alert. This addresses the garbage-in problem: the agent bases decisions on a clearer picture of the environment’s causal state.

3. Decision Engine (Causal-RL Agent):

The decision engine is an RL agent enhanced with access to the causal model. One way to implement this is a model-based RL approach: the agent can use the causal model as a simulator to evaluate action outcomes. For example, when the agent is considering an action (like “block traffic from IP X”), it can query the SCM: do(“block IP X”) and see what likely effects cascade (perhaps the model shows this will stop the data exfiltration cause, but also might disrupt a service if IP X was a critical server). The agent can simulate each candidate action in the causal model to estimate its reward outcome without having to actually execute it in the real system. This is essentially a form of planning with a causal model. With these what-if estimates, the agent chooses the action with the highest expected reward (taking into account both security benefit and side-effect costs as encoded in the reward function).

Another approach, exemplified by recent work like Q-Cogni, is to integrate causal structure discovery into the RL algorithm itself. Cunha et al. (2023) demonstrate that by allowing an RL agent to build a structural causal model of its environment, the agent achieved better learning efficiency and decision interpretability . In our context, this means as the agent interacts with the environment (in simulation or real incidents), it could refine the causal model – learning new cause-effect links or updating probabilities – which in turn makes its future decision-making more grounded. Essentially, the RL and causal inference parts form a feedback loop: the causal model informs the agent’s decisions, and the agent’s experiences update the causal model.

The decision engine would likely use a DRL algorithm (e.g., a variant of Q-learning, Actor-Critic, etc.) that is modified to incorporate the causal model’s predictions into the action-selection process. For instance, the agent’s Q-function (state-action value) could be augmented with features derived from causal reasoning (like predicted outcomes of the action). This hybrid could accelerate learning – because the agent is not learning blindly from trial-and-error; it has a strong prior in the form of the causal model. It also improves safety, as the agent can avoid obviously bad actions by consulting the model (e.g., if the model says action A causes a critical failure, the agent can prune that from its choices, thus never “experiencing” a disaster during training). Over time, as the RL agent learns from actual rewards, it will also adjust to any inaccuracies in the causal model, effectively fine-tuning the decision policy beyond what the initial model suggests.

4. Response Execution Module:

Once the decision engine selects an action (or sequence of actions) to respond to an incident, those actions are executed in the environment via the Response Module. This could interface with orchestration tools (for example, to isolate hosts, block IPs, increase logging, reboot systems, inform users, etc.). In a live system, certain actions might be fully automated while others could require human approval depending on autonomy level set by the organization (one could implement a slider for autonomy – e.g., fully automatic vs. human-in-the-loop). The outcome of the action (did it stop the attack? did it cause any negative side effects?) is observed by the sensors, closing the loop.

5. Feedback and Learning Loop:

The framework operates in a continuous feedback loop. After executing an action, the environment’s state changes, which is again observed and fed back. The causal model can be updated with this new data – importantly, this includes learning from interventions. If an action had an unexpected effect, the causal model may be revised to account for that new causal insight. Meanwhile, the RL agent receives a reward (for example, +100 if the attack was thwarted, -100 if data was stolen, small penalties if services went down, etc., as per the reward design), and this reward signals whether the action was good or bad in hindsight. The agent updates its policy accordingly (e.g., making it more likely to take similar actions in similar states if positive, or less likely if negative).
This feedback loop means the system improves over time. Early on, the causal model might be coarse and the agent’s policy suboptimal. But as more incidents are encountered (or simulated incidents during training), the system learns: the causal model becomes more refined in mapping how attacks propagate and how defenses mitigate, and the RL policy converges toward an optimal or at least effective strategy for incident response. The causal reasoning component ensures the learning is sample-efficient and safer by guiding the agent with informed predictions, while the RL component ensures the system doesn’t remain stuck in a priori assumptions – it can adjust if reality differs from the model.

6. Human-in-the-Loop and Interpretability:

Though automation is a goal, the framework should support human analysts in the loop, especially in high-stakes environments. One advantage of the causal component is that every decision can be accompanied by an explanation: the system can present the causal graph snippets that led to a certain decision (e.g., Detected event sequence A -> B -> C which causally indicates ransomware encryption in progress; chosen action is to isolate host because causal simulation showed it stops encryption with minimal business impact). Such explanations make it easier for a human analyst to either approve the action or intervene if something seems off. The RL agent’s policy might also be inspected via the causal model: for example, we can query the agent’s policy under different hypothetical scenarios to see what it would do, using the causal model to generate those scenarios. This is an emerging idea often called causal policy analysis, helping verify that the AI’s decisions align with expert expectations.
Furthermore, domain experts can update the causal knowledge base as new threat intelligence comes in, which will immediately influence the agent’s decisions (no need to re-train from scratch; the agent effectively has a new model to work with). This makes the system extensible and maintainable – a critical requirement in cybersecurity where new IOCs (Indicators of Compromise) and attack techniques are discovered continuously.

In essence, the integrated architecture marries two paradigms: (a) a top-down approach (causal models infused with expert knowledge and interpretable structure), and (b) a bottom-up approach (reinforcement learning that improves through experience). The causal part provides knowledge-driven guidance and the RL part provides experience-driven adaptation. Together, they aim to yield a decision-making system that is adaptive, explainable, and effective.
To illustrate the flow: imagine a new malware is spreading on the network. The anomaly detector flags unusual file encryption behavior on a host. The causal model identifies this as likely ransomware (due to a known causal pattern of file I/O and process behavior). The state given to the agent is “ransomware suspected on Host X, critical files at risk.” The RL agent, having been trained on many scenarios, knows that if you act quickly to isolate the host, you can save most files, but if you wait or just kill the process, the malware might spread or re-run. It queries the causal model to check the side effects of isolation (e.g., Host X runs a critical database – downtime might affect customers). Perhaps the model shows that Host X is indeed critical. The agent evaluates alternatives: isolation vs. attempting a system restore vs. doing nothing. It uses the causal model to foresee outcomes: isolation stops encryption but causes downtime, system restore might not fully remove the malware and could fail, doing nothing leads to total loss. It weighs the long-term reward: perhaps it has learned that isolating quickly yields the best reward (because saving data outweighs downtime cost). The agent selects “isolate Host X”. This is executed via the response module (cut off Host X’s network). Host X’s encryption stops, the attack is contained. The reward is high (attack foiled, some penalty for downtime). The agent learns maybe to improve by also scheduling an automatic failover for that database next time to reduce impact (if such an action is in its repertoire), which could be another action in future. The causal model is updated to note that isolating a host indeed prevented data loss (reinforcing that link in the graph). A report is generated explaining the cause (ransomware) and action (isolation) and the fact it saved the day.

This scenario shows how causal reasoning and RL together handle detection, diagnosis, decision, action, and learning in a loop. By integrating SCMs with RL, we essentially implement what some researchers term Causal Reinforcement Learning, where an agent can reason about “why” and “what if” within its learning process . This approach has been shown in other domains (like robotics or operations research) to improve learning efficiency and policy robustness , and here we tailor it to cybersecurity.

Use Cases and Potential Benefits of the Causal-RL Integration

To concretely demonstrate how combining causal reasoning with reinforcement learning can outperform existing methods, let us consider a few representative use cases in cybersecurity.

In each scenario, we contrast the performance of a traditional approach with the proposed integrated approach:

Use Case 1: DDoS Attack Detection and Mitigation

Scenario: A sudden surge in network traffic is detected on a web server, potentially indicating a Distributed Denial of Service (DDoS) attack.

• Traditional Approach: An anomaly-based NIDS (Network Intrusion Detection System) flags the high traffic as an anomaly and perhaps automatically triggers a rate limit or blackhole for that traffic. However, if the traffic surge was actually due to a flash crowd of legitimate users (a benign slashdot effect), this automatic response would cause a denial of service to real customers – a false positive outcome. Conversely, if it’s a slow-and-low DDoS that doesn’t cross static thresholds, the system might not trigger any response, letting the attack degrade service.

• Causal-RL Approach: A causal model helps determine the root cause of the traffic surge. For example, it might correlate the surge with a known pattern of DDoS (multiple source IPs, anomalous payloads) as opposed to a marketing event (traffic spike following a product announcement). Suppose the causal analysis finds that the anomaly is likely caused by a DDoS attack vector (e.g., many SYN packets causing half-open connections). The RL agent, which has experience dealing with DDoS in a simulated environment, considers response options: rate limiting, activating a scrubbing service, or doing nothing. Consulting the causal model, it predicts that rate limiting traffic will mitigate the attack with acceptable impact (because legitimate traffic can still get some throughput). It also sees that completely blocking all traffic from certain regions could cut off real users (a bad side effect). So the agent chooses to enable rate limiting on suspicious traffic patterns. This response is both adaptive and context-aware (it didn’t just blindly block everything, it used a nuanced strategy). The outcome is that the DDoS is blunted (service remains up, albeit with slightly reduced performance) and legitimate users can still access in smaller numbers. Over the incident, the agent might further adjust limits or apply learned rules if the attacker shifts tactics. This integrated approach outperforms a static IDS by accurately distinguishing malicious vs. benign surge (reducing false positive) and by deploying a tailored response (not too heavy-handed, avoiding unnecessary downtime).

In fact, the earlier cited study by Zeng et al. (2022) on DDoS detection aligns with this: their causal-infused detection was ~5% more accurate than classic ML, meaning the RL agent using that input would be acting on more reliable alerts. The RL component could additionally learn the optimal mitigation (maybe it learns an innovative throttling pattern that drops attack packets but lets legitimate packets through based on causal features). Overall, the service availability is higher and analyst intervention lower than would be with a conventional system that might oscillate between underreacting and overreacting.

Use Case 2: Insider Threat and Data Exfiltration

Scenario: An employee’s account is behaving unusually – accessing a large number of confidential files and sending data out of the network after hours. This could be an insider stealing data or a compromised account being used by an outside attacker.

• Traditional Approach: A UEBA (User and Entity Behavior Analytics) system might flag the activity as anomalous. However, it may not distinguish between a malicious exfiltration and, say, a legitimate but rare activity (perhaps the employee is running a backup or doing bulk data analysis). Most likely, an analyst would have to investigate the alert to decide if it’s an incident. If there’s automation, it might simply block the account or cut off access as a precaution, which, if the activity was legitimate, would hinder the employee’s work unnecessarily.
• Causal-RL Approach: The causal reasoning module considers various factors: the timing (after hours), the data volume, the types of files, and correlates with possible causes. Perhaps it knows that if an account was compromised via a phishing attack earlier (cause) it could lead to this pattern (effect). Indeed, say the causal model finds that the employee’s account logged in from an unusual location earlier (which could be a causal precursor to compromise). It thus infers a high probability this is a data theft incident. The RL agent receives a state like “Likely data exfiltration via insider account; asset sensitivity=high; user=regular employee not normally doing this.” The agent has actions like “lock account”, “trigger step-up authentication (MFA)”, “monitor quietly”, or “confiscate device” (if integrated with endpoint controls).
Instead of immediately locking the account (which stops exfiltration but alerts the adversary and could disrupt if it were a false alarm), the agent chooses a more nuanced action it learned: it triggers a multi-factor authentication challenge or an identity verification step for that account, effectively an action that a real analyst might do (“call the employee to confirm activity”). If the user fails the MFA (which an attacker likely will), the account is locked down – stopping the exfiltration. If the user passes (indicating it was actually them doing something legitimate), the system logs the event and perhaps alerts security to follow up but does not block the activity outright.

This response is adaptive and context-aware: it used an action (MFA challenge) that tests the hypothesis of compromise with minimal disruption. Traditional systems rarely have such capabilities. The causal-RL system thereby prevents data loss while minimizing impact on a legitimate user in case of a false alarm. In simulations or past incidents, the RL agent likely learned that MFA challenge yields high reward in these ambiguous scenarios because it often confirms an attack without the downsides of a full block.

This outperforms a purely anomaly-based or rule-based system by reducing both missed incidents and false positives. The incident response is essentially partially automated: the system handled it up to a point (MFA) and only if needed escalated to lock account. This lessens the load on security teams and contains threats faster.

Use Case 3: Automated Malware Containment on Endpoints

Scenario: A workstation is suspected to be infected with malware due to detection of suspicious processes and outgoing connections.

• Traditional Approach: The typical endpoint security might quarantine the device or kill the processes if they match known malware signatures. If it’s a known malware, this works. If not, a generic response might be to isolate the machine from the network and alert IT. That stops further spread, but the malware might have already done damage, and the user’s machine is now offline (perhaps unnecessarily if it was a false alarm). There’s no learning from this one-off event; it’s handled in isolation.

• Causal-RL Approach: The causal model identifies what type of malware behavior this most likely is (ransomware vs. botnet vs. Trojan) based on the pattern of process activity and network traffic. Let’s say it infers it’s likely a ransomware incident (encryption of files observed). It knows from causal relations that immediate isolation can prevent further encryption of network drives, but it also knows isolating means that remediation (like pushing a clean-up script remotely) might fail.
The RL agent, having trained on malware scenarios, considers a sequence: (a) suspend the suspicious process (if possible), (b) attempt a local remediation (like restore files or remove the malicious binary), and (c) only if that fails, isolate the machine. The agent tries step (a) and (b) automatically. Suppose it learned that many ransomware can be stopped by terminating the process and restoring encrypted files if caught early (high reward for saving files, low impact since machine not offline for long). If that works, the machine is clean and stays on the network, perhaps with a prompt to user to reboot. If it doesn’t work (maybe the malware resists termination), the agent escalates to (c) isolate the machine, and notifies IT for manual intervention.

Throughout, the causal model aids by predicting outcomes: it might simulate that if process termination fails (meaning the malware might have rootkit capabilities), then the best action is isolation to protect network drives (it’s learned the cause-effect that not isolating leads to spreading). The RL agent’s policy, informed by these predictions, smoothly transitions through actions. In contrast, a static system might have either done nothing until more damage or immediately isolated (which might have been unnecessary if simpler fix was possible). The integrated approach contains the malware earlier (because it tried actions as soon as suspicious behavior was confirmed causally as malware-like, rather than waiting for a signature or admin decision) and reduces downtime (because it doesn’t unnecessarily disconnect devices that can be cleaned online).
Additionally, every incident like this trains the agent more. If a new malware variant appears, even if not seen before, the combination of causal patterns (malware behavior) and the agent’s learned responses to similar patterns means it can likely handle it with only minor adjustments. Over time, the frequency of needing human intervention for malware incidents could drop dramatically, as the agent gets better – a clear improvement over static playbooks.

Use Case 4: Proactive Defense with Attack Graphs

Scenario: The organization uses an attack graph to model its network vulnerabilities and possible attack paths (from an internet-facing server to a database). There are known vulnerabilities on some machines that, if exploited in sequence, could lead to a major breach.

• Traditional Approach: Security teams might manually harden the network or set static rules (like network segmentation) to mitigate these known paths. If an alert comes that one vulnerability is being exploited, they react by isolating that segment. Proactive measures like moving target defense or dynamic reconfiguration are typically not automated in traditional setups.

• Causal-RL Approach: In our framework, the attack graph essentially serves as a causal model of attacker behavior (exploitation of vulnerability A leads to foothold, which enables exploiting B, etc.). The RL agent can be tasked not only with responding to detected incidents, but with proactively reducing risk. For example, it could periodically take actions to minimize the probability of attack success, like applying patches, changing firewall rules, or diverting attacker traffic to honeypots, guided by the attack graph. If the causal model indicates a particularly critical causal chain (say A -> B -> C leads to crown jewels), the agent may focus on breaking that chain by either patching A or strengthening monitoring on B and C. Over time, the agent learns which proactive steps yield the highest reward in terms of security posture (perhaps patching certain systems, or periodically rotating credentials, etc.).
Now, when an attacker actually attempts the first step (exploiting A), the system is already prepared – maybe the agent had pre-emptively isolated that vulnerable service until patch, or set a tarp (deception) there. So either the attack is foiled outright or quickly detected and contained. This outperforms a reactive approach by preventing incidents or catching them at the earliest point.

Essentially, the causal-RL system works continuously, not just when an alert pops up. It learns to manage the security state as a whole (like a game where it tries to keep the system out of the “checkmate” conditions of an attack path). Traditional methods lacking such adaptive planning would only act once an attack is underway, losing precious time and opportunity to blunt the attack before it escalates.

Use Case 5: Continuous Alert Triage and Analyst Assist

Scenario: A Security Operations Center (SOC) receives thousands of alerts per day from various tools. Many are false positives. Analysts spend time triaging these, often using intuition and context to decide which are worth investigating.

• Traditional Approach: Maybe a simple automation or SIEM correlation reduces some noise, but largely it’s manual. Some alerts get missed or delayed due to volume.

• Causal-RL Approach: The system can treat alert triage as a sequential decision problem: for each alert (state) it decides “escalate to human” or “suppress/close” (action). The reward could be defined based on outcomes (true attacks escalated = positive, false alarms escalated = negative due to wasted time, true attacks suppressed = highly negative). The causal model can incorporate context like “multiple alerts on same host in short time -> likely real incident” or “alert corresponds to known benign pattern -> likely false”. Using that, the RL agent learns a policy for alert handling. Over time, it might achieve, for example, an autonomous closure of 80% of alerts that are indeed false positives, and correctly bubble up the critical 20% to humans, with very few misses. Essentially, it’s learning from historical incidents and feedback which combinations of alerts lead to real incidents (a causal relationship) and which don’t. As a result, the mean time to respond goes down because the system filters and reacts to alerts faster, and analysts are freed from tedious false alarms. This goes beyond static correlation rules by learning complex patterns and continuously updating its triage policy as new forms of alerts come in.

In each of these use cases, the common theme is that the integration of causal reasoning and RL leads to decisions that are more accurate, timely, and tailored to the situation than conventional methods. False positives are reduced by causal context (the system “understands” when something is normal vs. malicious better), and false negatives are reduced by RL adaptivity (the system learns to catch what static logic might miss). Responses are smarter – not one-size-fits-all, but taking into account likely causes and effects, thereby avoiding overreactions and underreactions.

It is important to note that while these use cases are promising, real-world deployment would require rigorous testing and gradual introduction (perhaps starting with the system giving recommendations to human analysts, and later automating once trust is built). Fortunately, there are already glimmers of this future in practice. For example, some advanced SOC platforms are incorporating automated reasoning engines that suggest likely root causes of incidents (an aspect of causal reasoning). Companies like Darktrace have deployed self-learning (if not full RL, at least adaptive) systems that can take limited autonomous actions (like “Antigena” which responds to threats in real time). Our proposed approach can be seen as the next evolutionary step: combining those adaptive responses with a causal brain. As an analogy, if current ML security tools are like reflexes, a causal-RL system is like a trained reflex guided by reasoning – quick to act, but with an understanding of what it’s doing.

Conclusion

Current cybersecurity defenses, while bolstered by machine learning for anomaly detection, often behave like smoke alarms – they can raise alerts at the hint of trouble but cannot discern the fire from the burnt toast. High false positives, lack of explainability, static detection models, and simplistic response playbooks leave security teams stretched and attackers with the advantage. This white paper argued that integrating causal reasoning and reinforcement learning can fundamentally improve automated decision-making and incident response in cybersecurity by making defenses both smarter and more adaptive.
Causal reasoning addresses the “brain” of the system – providing a deep understanding of cause and effect in cyber events. It enables pinpointing the root causes of anomalies, filtering out noise, incorporating expert knowledge (e.g., known system dependencies and attack paths), and predicting the outcomes of potential defensive actions. Reinforcement learning, on the other hand, endows the system with “muscle memory” – the ability to learn from interaction and optimize response policies over time. It allows the defense to dynamically adjust to new threats and complex scenarios that static rules cannot cover, effectively learning by doing in a controlled way.

Together, a causal-RL framework leverages the strengths of both: the causal component ensures decisions are informed and interpretable, while the RL component ensures the system can improve itself and handle novelty. By using structural causal models (SCMs) as an underlying representation, the system can perform root cause analysis of alerts and simulate “what-if” scenarios for response options, addressing the longstanding challenge of interpretability and strategic planning in automated defense. The RL agent, guided by this model, can choose optimal actions and even proactively harden the environment, addressing the challenge of adaptation and decision-making under uncertainty.

In conclusion, causal reasoning and reinforcement learning are not just two more buzzwords to add to cybersecurity, but together they form a complementary pair – the analyst and the operator – within an AI system. Causal reasoning provides the analyst’s insight (why is something happening? what should I consider?), and reinforcement learning provides the operator’s instinct (what action will achieve the best outcome?). By fusing these, we can create cybersecurity defense systems that are as adaptive and cunning as the threats they face, yet as accountable and transparent as the security professionals who oversee them. This paves the way for a new generation of cybersecurity solutions that can keep pace with, and eventually outmaneuver, the ever-evolving adversaries in cyberspace.

References

Ahmed, M., Mahmood, A. N., & Hu, J. (2016). A survey of network anomaly detection techniques. Journal of Network and Computer Applications, 60, 19–31. (Identified high false positive rates in anomaly-based intrusion detection).
Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys, 41(3), 15. (Provided a comprehensive overview of anomaly detection techniques and challenges such as data quality).
Cichonski, P., Millar, T., Grance, T., & Scarfone, K. (2012). Computer Security Incident Handling Guide (NIST SP 800-61 Rev. 2). NIST. (Defined the standard phases of incident response).
Cunha, C., Liu, W., French, T., & Mian, A. (2023). Q-Cogni: An* Integrated Causal Reinforcement Learning Framework.* arXiv:2302.13240. (Demonstrated integrating structural causal models into Q-learning to improve learning efficiency and decision interpretability in an RL context).
Dhir, N., Hoeltgebaum, H., Adams, N., Briers, M., Burke, A., & Jones, P. (2021). Prospective Artificial Intelligence Approaches for Active Cyber Defence. Proceedings of IEEE CogSIMA Workshop on Active Cyber Defense (position paper). (Highlighted reinforcement learning and causal inference as promising approaches for active cyber defense and discussed causal representations for attack prediction).
Emmert, J. (2025, March 27). Howso: Leading the Charge in Anomaly Detection. Howso Blog. (Industry perspective on using causal AI for anomaly detection, noting limitations of black-box models, lack of context, and static analysis, and the importance of root-cause explainability).
Purves, T., Kyriakopoulos, K. G., Jenkins, S., Phillips, I. W., & Dudman, T. (2024). Causally aware reinforcement learning agents for autonomous cyber defence. Knowledge-Based Systems, 304, 112521. (Introduced a framework for integrating causal modeling with deep reinforcement learning for network defense, showing improved performance over standard RL). (Referencing details from semantic summary).
Quantum News. (2025, March 27). Multi-Objective Reinforcement Learning in Cybersecurity Defense Against Diverse Threats. QuantumZeitgeist.com. (Discussed how deep reinforcement learning, e.g. PPO with intrinsic exploration, can adapt to evolving cyber attacks and optimize multiple objectives like detection speed and resource usage in dynamic environments).
Zeng, Z., Peng, W., Zeng, D., Zeng, C., & Chen, Y. (2022). Intrusion detection framework based on causal reasoning for DDoS. Journal of Information Security and Applications, 65, 103124. (Demonstrated that using causal inference – do-operation based feature selection and counterfactual analysis – in DDoS detection reduces false associations and improves accuracy by ~5% compared to correlation-based ML methods).