Multi-stage Attack Detection and Prediction Using Graph Neural Networks: An IoT Feasibility Study
<|reference_start|>Multi-stage Attack Detection and Prediction Using Graph Neural Networks: An IoT Feasibility Study: With the ever-increasing reliance on digital networks for various aspects of modern life, ensuring their security has become a critical challenge. Intrusion Detection Systems play a crucial role in ensuring network security, actively identifying and mitigating malicious behaviours. However, the relentless advancement of cyber-threats has rendered traditional/classical approaches insufficient in addressing the sophistication and complexity of attacks. This paper proposes a novel 3-stage intrusion detection system inspired by a simplified version of the Lockheed Martin cyber kill chain to detect advanced multi-step attacks. The proposed approach consists of three models, each responsible for detecting a group of attacks with common characteristics. The detection outcome of the first two stages is used to conduct a feasibility study on the possibility of predicting attacks in the third stage. Using the ToN IoT dataset, we achieved an average of 94% F1-Score among different stages, outperforming the benchmark approaches based on Random-forest model. Finally, we comment on the feasibility of this approach to be integrated in a real-world system and propose various possible future work.<|reference_end|>
@article{friji2024multi-stage,
    title={Multi-stage Attack Detection and Prediction Using Graph Neural Networks:
  An IoT Feasibility Study},
    author={Hamdi Friji, Ioannis Mavromatis, Adrian Sanchez-Mompo, Pietro
  Carnelli, Alexis Olivereau, Aftab Khan},
    journal={arXiv preprint arXiv:2404.18328},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.18328},
    primaryClass={cs.CR cs.AI}
}