CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks

Advanced Persistent Threats (APTs) represent a significant challenge in cybersecurity due to their sophisticated and stealthy nature. Traditional Intrusion Detection Systems (IDS) often fall short in detecting these multi-stage attacks. Recently, Graph Neural Networks (GNNs) have been employed to enhance IDS capabilities by analyzing the complex relationships within networked data. However, existing GNN-based solutions are hampered by high false positive rates and substantial resource consumption. In this paper, we present a novel IDS designed to detect APTs using a Spatio-Temporal Graph Neural Network Autoencoder. Our approach leverages spatial information to understand the interactions between entities within a graph and temporal information to capture the evolution of the graph over time. This dual perspective is crucial for identifying the sequential stages of APTs. Furthermore, to address privacy and scalability concerns, we deploy our architecture in a federated learning environment. This setup ensures that local data remains on-premise while encrypted model-weights are shared and aggregated using homomorphic encryption, maintaining data privacy and security. Our evaluation shows that this system effectively detects APTs with lower false positive rates and optimized resource usage compared to existing methods, highlighting the potential of spatio-temporal analysis and federated learning in enhancing cybersecurity defenses.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs

This research investigates the effectiveness of established vulnerability metrics, such as the Common Vulnerability Scoring System (CVSS), in evaluating attacks against Large Language Models (LLMs), with a focus on Adversarial Attacks (AAs). The study explores the influence of both general and specific metric factors in determining vulnerability scores, providing new perspectives on potential enhancements to these metrics. This study adopts a quantitative approach, calculating and comparing the coefficient of variation of vulnerability scores across 56 adversarial attacks on LLMs. The attacks, sourced from various research papers, and obtained through online databases, were evaluated using multiple vulnerability metrics. Scores were determined by averaging the values assessed by three distinct LLMs. The results indicate that existing scoring-systems yield vulnerability scores with minimal variation across different attacks, suggesting that many of the metric factors are inadequate for assessing adversarial attacks on LLMs. This is particularly true for context-specific factors or those with predefined value sets, such as those in CVSS. These findings support the hypothesis that current vulnerability metrics, especially those with rigid values, are limited in evaluating AAs on LLMs, highlighting the need for the development of more flexible, generalized metrics tailored to such attacks. This research offers a fresh analysis of the effectiveness and applicability of established vulnerability metrics, particularly in the context of Adversarial Attacks on Large Language Models, both of which have gained significant attention in recent years. Through extensive testing and calculations, the study underscores the limitations of these metrics and opens up new avenues for improving and refining vulnerability assessment frameworks specifically tailored for LLMs.