“Autonomous Security Monitoring, Threat Hunting, and Adversary Detection” as a research field merges concepts from machine learning, cybersecurity, and network analysis to create solutions that can proactively and reactively address cyber threats without or with minimal human intervention. Examples of research projects in this area are:
- Autonomous Security Monitoring: This involves developing systems that continuously monitor network and system activities to detect any anomalies or signs of cyber threats. These systems use advanced algorithms to analyze vast amounts of data in real-time and identify potential security breaches.
- Automated Threat Hunting: This aspect focuses on proactively searching through networks to detect and isolate advanced threats that evade existing security measures. It involves predictive analytics and machine learning to anticipate and identify unusual patterns or behaviors that could signify a cyber attack. Other research topics in this domain are developing tools that can autonomously hunt for threats within a network and use advanced algorithms to scan, analyze, and identify hidden threats.
- Autonomous Adversary Detection: Involves identifying and understanding attackers’ methodologies and strategies. This includes creating systems that can adapt to evolving tactics used by cybercriminals and using AI to learn from past attacks and predict future ones. Moreover, research into behavioral analysis techniques can identify malicious actors based on their actions and tactics, even when they use sophisticated methods to hide their tracks.
- Self-Learning Cybersecurity Systems: Projects that aim to develop systems capable of self-learning from past attacks and automatically updating their defense mechanisms to protect against future threats.
- Simulation and Red Teaming Automation: Creating automated systems for simulating attacks (red teaming) to test and improve the effectiveness of autonomous security systems.