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Deep Learning Techniques to Detect DoS Attacks on Industrial Control Systems: a Systematic Literature Review

EasyChair Preprint no. 6233

8 pagesDate: August 5, 2021


Cyber Physical Systems (CPS) security is crucial demand within industrial fields. The deployment of these systems within critical infrastructure is increasing day by day. CPS applications include smart grid, Industrial Control Systems (ICS), aerial systems and Intelligent Transportation Systems (ITS). The complexity, heterogeneity, and diversity evolved with these CPS systems. In addition, the inter-connectivity of these systems over cyberspace has increased their attack surface. This research paper provides a survey on deep learning detection techniques for the Denial of Service (DoS) attack, which is considered the most critical and major attack on CPS. Moreover, the survey study demonstrates the most used deep learning techniques in the research articles of traditional IT networks and ICS networks. It also considers their used datasets as a training sources and their most common evaluation matrix used to benchmark their performance against each other. Besides, it has identified a research gap related to classifier efficiency, while considering modern dataset related to ICS protocols. Moreover, consider the actual cyberspace attack traffic collected from passive monitoring sensors. This would resolve the need for using less features provided over outdated and publicly available dataset.

Keyphrases: anomaly detection, attack detection, deep learning, DoS, DoS attack, ICS, Industrial Control System, passive monitoring, used deep learning technique

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Abdalkarim R. Seyam and Ali Bou Nassif and Qassim Nasir and Bushra Al Blooshi and Manar Abu Talib},
  title = {Deep Learning Techniques to Detect DoS Attacks on Industrial Control Systems: a Systematic Literature Review},
  howpublished = {EasyChair Preprint no. 6233},

  year = {EasyChair, 2021}}
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