Ideal cyber threat intelligence (CTI) includes insights into attacker strategies that are specific
to a network under observation. Such CTI currently requires extensive expert input for …

Machine Learning (ML) has become a valuable asset to solve many real-world tasks. For
Network Intrusion Detection (NID), however, scientific advances in ML are still seen with …

We consider interdependent systems managed by multiple defenders that are under the
threat of stepping-stone attacks. We model such systems via game-theoretic models and …

Although intrusion alerts can provide threat intelligence regarding attacker strategies,
extracting such intelligence via existing tools is expensive and time-consuming. Earlier work …

Substations are critical components of the smart grid since compromising them can lead to
significant consequences, such as blackouts. Threat modeling aims to model different ways …

Decision-making in cyber security attack scenarios involves deep uncertainty and
adversarial decision-making. Robust Decision Making (RDM) uses a structured approach to …

The evolving nature of the tactics, techniques, and procedures used by cyber adversaries
have made signature and template based methods of modeling adversary behavior almost …

Facing increasingly sophisticated attacks from external adversaries, interdependent systems
owners have to judiciously allocate their (often limited) security budget in order to reduce …

The attack graph model is an effective method for analyzing multi-step attacks. Most existing
related works only focus on networks with static topology and are unsuitable for the satellite …

This research paper focuses on the complex domain of alert-driven attack graphs. SAGE is a
tool which generates such attack graphs (AGs) by using a suffix-based probabilistic …