AI algorithms and machine learning can sift through vast amounts of data efficiently and relatively quickly. This helps network defenders sift through the endless supply of alerts and identify those that pose a potential threat (as opposed to false positives). Reinforcement learning underpins the benefits of AI to the cybersecurity ecosystem and is closest to how humans learn through experience and trial and error.
Unlike supervised learning, reinforcement learning focuses on how agents can learn from their actions and feedback in the environment. Reinforcement learning is the idea of using rewards and punishments to calculate positive and negative behaviors to maximize their performance over time. Enough information is collected to make the best decisions in the future.
How reinforcement learning can help
Security operations center (SOC) analyst alert fatigue has become a legitimate business concern for chief information security officers, resulting in analyst burnout and employee turnover. A solution that can handle most of the alert “noise” allows analysts to prioritize the real threats, saving organizations both time and money.
AI capabilities can help mitigate the threat posed by attacks such as large-scale social engineering, phishing, and spam campaigns by understanding and recognizing the kill chain of these attacks before they succeed. This is important given the security resource constraints experienced by most organizations, regardless of size or budget.
More advanced dynamic attacks are a greater challenge and may be used only a limited number of times before the attacker adjusts or changes parts of the attack sequence. Here, reinforcement learning allows you to study attack cycles and identify relevant patterns from failed attacks and previous successful attacks. The more sophisticated attacks and their diverse iterations you are exposed to, the better positioned reinforcement learning will be to identify them in real time.
Admittedly, there will be a learning curve initially, especially if attackers change their attack methods frequently. But parts of the attack chain remain and are good data points to drive the process.
From detection to prediction
detection is just one part of threat monitoring. AI reinforcement learning has the potential to be applied to the following fields: prediction To prevent attacks, we learn from past experiences and weak signals and use patterns to predict what will happen next time.
Cyber threat prevention is a natural progression from passive detection and a necessary progression to make cybersecurity proactive rather than reactive. Reinforcement learning can enhance the capabilities of cybersecurity products by making optimal decisions based on threats. This not only streamlines the response, but also maximizes available resources through optimal allocation, coordination with other cybersecurity systems in the environment, and deployment of countermeasures. With continuous feedback and cycles of rewards and punishments, prevention becomes more robust and effective the longer it is used.
Reinforcement learning use cases
One use case for reinforcement learning is network monitoring. In this case, agents can detect network intrusions by observing traffic patterns and applying lessons learned to generate alerts. Reinforcement learning can go a step further by taking action to block or redirect traffic. This is especially effective against botnets, where reinforcement learning can learn communication patterns and devices within a network and then base their best countermeasures on disrupting them.
AI reinforcement learning can also be applied in a virtual sandbox environment where you can analyze how malware behaves, and can aid in patch management cycles for vulnerability management.
Reinforcement learning comes with unique challenges
One immediate concern is the continued addition of devices to networks, creating more endpoints to protect. This situation is further exacerbated by remote work situations and the permitted use of personal devices in professional environments. As devices are continually added, it becomes increasingly difficult for machine learning to account for all potential entry points for attacks. Zero trust approaches alone can pose intractable challenges, but when synergized with AI reinforcement learning, they can deliver strong and flexible IT security.
Another challenge is accessing enough data to detect patterns and take action. Initially, the amount of data available to exploit and process may be insufficient, which may distort the learning cycle or flaw defensive measures.
This can have implications when dealing with attackers who deliberately manipulate data to fool the learning cycle and influence the “truth” of the initial information. As more AI reinforcement learning algorithms are integrated into cybersecurity technology, this must be considered. Threat actors must be innovative and willing to think outside the box.
Contributor: Emilio Iasiello, Dentons Global Cyber Threat Intelligence Manager
