[Paper Review] Use of Graph Neural Networks in Aiding Defensive Cyber Operations

Shaswata Mitra, Trisha Chakraborty+3 more|arXiv (Cornell University)|Jan 11, 2024

Information and Cyber Security4 citations

TL;DR

This paper proposes using Graph Neural Networks (GNNs) to enhance defensive cyber operations by disrupting each stage of the Lockheed Martin Cyber Kill Chain. GNNs analyze graph-structured cyber threat data to detect patterns, predict attacker behavior, and improve detection and response across the attack lifecycle, offering a novel, comprehensive framework for intelligent cyber defense with identified research gaps and future directions.

ABSTRACT

In an increasingly interconnected world, where information is the lifeblood of modern society, regular cyber-attacks sabotage the confidentiality, integrity, and availability of digital systems and information. Additionally, cyber-attacks differ depending on the objective and evolve rapidly to disguise defensive systems. However, a typical cyber-attack demonstrates a series of stages from attack initiation to final resolution, called an attack life cycle. These diverse characteristics and the relentless evolution of cyber attacks have led cyber defense to adopt modern approaches like Machine Learning to bolster defensive measures and break the attack life cycle. Among the adopted ML approaches, Graph Neural Networks have emerged as a promising approach for enhancing the effectiveness of defensive measures due to their ability to process and learn from heterogeneous cyber threat data. In this paper, we look into the application of GNNs in aiding to break each stage of one of the most renowned attack life cycles, the Lockheed Martin Cyber Kill Chain. We address each phase of CKC and discuss how GNNs contribute to preparing and preventing an attack from a defensive standpoint. Furthermore, We also discuss open research areas and further improvement scopes.

Motivation & Objective

• Address the growing complexity and evolution of cyberattacks that evade traditional signature-based detection methods.
• Investigate the potential of Graph Neural Networks (GNNs) to model complex, relational cyber threat data for improved defensive operations.
• Map GNN applications across all phases of the Cyber Kill Chain to provide a systematic, lifecycle-based defense framework.
• Identify open challenges in GNN deployment for cybersecurity, including data integration, privacy, and model generalization.
• Outline future research directions for advancing GNN-based cyber defense systems through knowledge sharing and collaborative learning.

Proposed method

• Model cyber threat data as heterogeneous graphs, representing entities (e.g., systems, users, IPs) and their interactions (e.g., network traffic, file access).
• Apply GNNs to learn node and graph-level representations by aggregating neighborhood information through message-passing mechanisms.
• Utilize GNNs for anomaly detection by identifying deviations in graph structure or node embeddings indicative of malicious behavior.
• Integrate GNNs into threat intelligence platforms to enrich knowledge graphs with contextual, relational insights from diverse data sources.
• Explore federated and transfer learning techniques to enable collaborative model training while preserving privacy and enabling knowledge sharing.
• Leverage GNNs for fake data detection and graph structure obfuscation mitigation to improve trustworthiness of shared threat intelligence.

Figure 1 . Illustration of GNN aggregate and update functions. The nodes $v$ of input graph $G$ is aggregates the embeddings $h_{u}^{(k)}$ over $k$ iteration from the neighbourhood $\mathcal{N}(v)$ . Then, the node embedding of node $v$ is updated which is denoted by $a_{v}$ . The final updated node

Experimental results

Research questions

• RQ1 How can GNNs be effectively applied to detect and disrupt each phase of the Cyber Kill Chain?
• RQ2 What are the key advantages of GNNs over traditional ML and rule-based methods in modeling complex cyber threat relationships?
• RQ3 How can GNNs support real-time threat intelligence sharing while preserving data privacy and integrity?
• RQ4 What are the major technical and operational challenges in deploying GNNs for large-scale defensive cyber operations?
• RQ5 What future research directions can enhance the robustness, generalization, and scalability of GNN-based cyber defense systems?

Key findings

• GNNs effectively model the relational and hierarchical nature of cyber threat data, enabling detection of subtle attack patterns invisible to traditional methods.
• GNNs enhance detection across all phases of the Cyber Kill Chain, from initial reconnaissance to exfiltration, by learning from graph-structured system interactions.
• Knowledge sharing among GNN models through federated learning and graph-based benchmarks can improve collective threat detection and reduce false positives.
• Despite promise, GNNs face challenges in handling graph obfuscation techniques like NOP insertion and subroutine reordering, requiring adaptive learning strategies.
• Current research lacks standardized, open-source datasets and evaluation benchmarks for GNN-based cyber defense, limiting reproducibility and progress.
• Privacy-preserving techniques such as graph structure hiding are underexplored in GNN-based threat intelligence sharing, representing a critical research gap.

Figure 2 . Overview of our proposed taxonomy. We considered seven phases of the cyber kill chain (CKC) [Reconnaissance, Weaponization, Delivery, Exploitation, Installation, Command & Control, Actions on Objectives] with possible attacker activities. For prevention, we consider seven defensive phases