2019/05/08 by Gael Kamdem De Teyou, De Teyou, Gael Kamdem, Junior Ziazet +1
Computer Science · Engineering · #Advanced Malware Detection Techniques #Anomaly Detection Techniques and Applications #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Network Security and Intrusion Detection #Smart Grid Security and Resilience
paper · pdf · doi:10.48550/arxiv.1905.03168
openalex publication_date 2019/05/08 · openalex created_date 2022/07/29 · openalex updated_date 2026/07/28
The extensive use of Information and Communication Technology in critical\ninfrastructures such as Industrial Control Systems make them vulnerable to\ncyber-attacks. One particular class of cyber-attacks is advanced persistent\nthreats where highly skilled attackers can steal user authentication\ninformation's and move in the network from host to host until a valuable target\nis reached. The detection of the attacker should occur as soon as possible in\norder to take appropriate response, otherwise the attacker will have enough\ntime to reach sensitive assets. When facing intelligent threats, intelligent\nsolutions have to be designed. Therefore, in this paper, we take advantage of\nrecent progress in deep learning to build a convolutional neural networks that\ncan detect intrusions in cyber physical system. The Intrusion Detection System\nis applied on the NSL-KDD dataset and the performances of the proposed approach\nare presented and compared with the state of art. Results show the\neffectiveness of the techniques.\n