Finnian Westenfelder, Erik Hemberg, Stephen Moskal, Una-May O’Reilly, Silviu Chiricescu. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

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 obtaining, assessing, and correlating system vulnerabilities into a graphical representation, often referred to as an attack graph (AG). Instead of deriving AGs based on system vulnerabilities, this work advocates the direct use of intrusion alerts. We propose SAGE, an explainable sequence learning pipeline that automatically constructs AGs from intrusion alerts without a priori expert knowledge. SAGE exploits the temporal and probabilistic dependence between alerts in a suffix-based probabilistic deterministic finite automaton (S-PDFA)-a model that brings infrequent severe alerts into the spotlight and summarizes paths leading to them. Attack graphs are extracted from the model on a per-victim, per-objective basis. SAGE is thoroughly evaluated on three open-source intrusion alert datasets collected through security testing competitions in order to analyze distributed multi-stage attacks. SAGE compresses over 330k alerts into 93 AGs that show how specific attacks transpired. The AGs are succinct, interpretable, and provide directly relevant insights into strategic differences and fingerprintable paths. They even show that attackers tend to follow shorter paths after they have discovered a longer one in 84.5% of the cases.

Attack graphs (AG) are a popular area of research that display all the paths an attacker can exploit to penetrate a network. Existing techniques for AG generation rely heavily on expert input regarding vulnerabilities and network topology. In this work, we advocate the use of AGs that are built directly using the actions observed through intrusion alerts, without prior expert input. We have developed an unsupervised visual analytics system, called SAGE, to learn alert-driven attack graphs. We show how these AGs (i) enable forensic analysis of prior attacks, and (ii) enable proactive defense by providing relevant threat intelligence regarding attacker strategies. We believe that alert-driven AGs can play a key role in AI-enabled cyber threat intelligence as they open up new avenues for attacker strategy analysis whilst reducing analyst workload.

Recognizing the need for proactive analysis of cyber adversary behavior, this paper presents a new event-driven simulation model and implementation to reveal the efforts needed by attackers who have various entry points into a network. Unlike previous models which focus on the impact of attackers' actions on the defender's infrastructure, this work focuses on the attackers' strategies and actions. By operating on a request-response session level, our model provides an abstraction of how the network infrastructure reacts to access credentials the adversary might have obtained through a variety of strategies. We present the current capabilities of the simulator by showing three variants of Bronze Butler APT on a network with different user access levels.

Intrusion detection system (IDS) is an integral part of computer networks to monitor and detect threats. However, the alerts raised by these systems are often overwhelming to security analysts, making it difficult to uncover the steps an attacker took to compromise one or more systems in the network. This work presents a novel approach that aggregates IDS alerts and forms sequences of attack activities and their corresponding probabilistic models. This allows comparison of attack sequences to offer insights for unique as well as similar attack behaviors. We aggregate alerts by performing a Gaussian filter on specific alert attributes and model attackers using a suffix-based probabilistic model. We compare sequences generated from ten independent attacking teams with similar objectives demonstrating how our process uncovers similarities and uniqueness between the attacks that was not obvious. The sequences revealed by our process creates meaningful sequences that offers insights on how the attacking teams exploit a network.