Corentin Larroche

Corentin Larroche

Foundation models have recently emerged as a new paradigm in machine learning (ML). These models are pre-trained on large and diverse datasets and can subsequently be applied to various downstream tasks with little or no retraining. This allows people without advanced ML expertise to build ML applications, accelerating innovation across many fields. However, the adoption of foundation models in cybersecurity is hindered by their inability to efficiently process data such as network traffic captures or binary executables. The recent introduction of graph foundation models (GFMs) could make a significant difference, as graphs are well-suited to representing these types of data. We study the usability of GFMs in cybersecurity through the lens of one specific use case, namely lateral movement detection. Using a pre-trained GFM, we build a detector that reaches state-of-the-art performance without requiring any training on domain-specific data. This case study thus provides compelling evidence of the potential of GFMs for cybersecurity.

Corentin Larroche, Johan Mazel, Stephan Clémençon

Anomaly detection in event logs is a promising approach for intrusion detection in enterprise networks. By building a statistical model of usual activity, it aims to detect multiple kinds of malicious behavior, including stealthy tactics, techniques and procedures (TTPs) designed to evade signature-based detection systems. However, finding suitable anomaly detection methods for event logs remains an important challenge. This results from the very complex, multi-faceted nature of the data: event logs are not only combinatorial, but also temporal and heterogeneous data, thus they fit poorly in most theoretical frameworks for anomaly detection. Most previous research focuses on either one of these three aspects, building a simplified representation of the data that can be fed to standard anomaly detection algorithms. In contrast, we propose to simultaneously address all three of these characteristics through a specifically tailored statistical model. We introduce \textsc{Decades}, a \underline{d}ynamic, h\underline{e}terogeneous and \underline{c}ombinatorial model for \underline{a}nomaly \underline{d}etection in \underline{e}vent \underline{s}treams, and we demonstrate its effectiveness at detecting malicious behavior through experiments on a real dataset containing labelled red team activity. In particular, we empirically highlight the importance of handling the multiple characteristics of the data by comparing our model with state-of-the-art baselines relying on various data representations.