Multi-stage Attack Detection and Prediction Using Graph Neural Networks: An IoT Feasibility Study
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arXiv
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| Format: | Preprint |
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2024
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| author | Friji, Hamdi Mavromatis, Ioannis Sanchez-Mompo, Adrian Carnelli, Pietro Olivereau, Alexis Khan, Aftab |
| author_facet | Friji, Hamdi Mavromatis, Ioannis Sanchez-Mompo, Adrian Carnelli, Pietro Olivereau, Alexis Khan, Aftab |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_18328 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-stage Attack Detection and Prediction Using Graph Neural Networks: An IoT Feasibility Study Friji, Hamdi Mavromatis, Ioannis Sanchez-Mompo, Adrian Carnelli, Pietro Olivereau, Alexis Khan, Aftab Cryptography and Security Artificial Intelligence 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. |
| title | Multi-stage Attack Detection and Prediction Using Graph Neural Networks: An IoT Feasibility Study |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2404.18328 |