(Publisher of Peer Reviewed Open Access Journals)

International Journal of Advanced Technology and Engineering Exploration (IJATEE)

ISSN (Print):2394-5443    ISSN (Online):2394-7454
Volume-5 Issue-49 December-2018
Full-Text PDF
DOI:10.19101/IJATEE.2018.547030
Paper Title : Deep learning algorithm based cyber-attack detection in cyber-physical systems-a survey
Author Name : Valliammal N. and Barani Shaju
Abstract :

Over the last years, cyber-attack detection and control system design has become a significant area in cyber-physical systems (CPSs) due to the rapid growth of cyber-security challenges via sophisticated attacks like data injection attacks, replay attacks, etc. The effect of different attacks may provide system failure, malfunctioning, etc. As a result, an improved security system may require to implement the cyber defense system for upcoming CPSs. The different deep learning algorithm based cyber-attack detection schemes have been designed to detect and mitigate the different types of cyber-attacks through CPSs, smart grids, power systems, etc. This article presents a detailed survey of various deep learning algorithms proposed for CPSs to achieve cyber defense. At first, different algorithms developed by previous researchers are studied in detail. Then, a comparative analysis is carried out to know the limitations in each algorithm and provide a suggestion for further improvement of CPSs with more efficiently.

Keywords : Cyber-physical systems, Cyber-attacks, Cyber-security, Deep learning algorithms.
Cite this article : Valliammal N. and Barani Shaju, " Deep learning algorithm based cyber-attack detection in cyber-physical systems-a survey " , International Journal of Advanced Technology and Engineering Exploration (IJATEE), Volume-5, Issue-49, December-2018 ,pp.489-494.DOI:10.19101/IJATEE.2018.547030
References :
[1]Majhi SK, Patra G, Dhal SK. Cyber physical systems & public utility in India: state of art. Procedia Computer Science. 2016; 78:777-81.
[Crossref] [Google Scholar]
[2]Sebestyen G, Hangan A. Anomaly detection techniques in cyber-physical systems. Acta Universitatis Sapientiae, Informatica. 2017; 9(2):101-18.
[Crossref] [Google Scholar]
[3]Ozay M, Esnaola I, Vural FT, Kulkarni SR, Poor HV. Machine learning methods for attack detection in the smart grid. IEEE Transactions on Neural Networks and Learning Systems. 2016; 27(8):1773-86.
[Crossref] [Google Scholar]
[4]Hodo E, Bellekens X, Hamilton A, Dubouilh PL, Iorkyase E, Tachtatzis C, et al. Threat analysis of IoT networks using artificial neural network intrusion detection system. In international symposium on networks, computers and communications 2016 (pp. 1-6). IEEE.
[Google Scholar]
[5]Goh J, Adepu S, Tan M, Lee ZS. Anomaly detection in cyber physical systems using recurrent neural networks. In international symposium on high assurance systems engineering 2017 (pp. 140-5). IEEE.
[Crossref] [Google Scholar]
[6]Kreimel P, Eigner O, Tavolato P. Anomaly-based detection and classification of attacks in cyber-physical systems. In proceedings of the international conference on availability, reliability and security 2017. ACM.
[Crossref] [Google Scholar]
[7]Ghanbari M, Kinsner W, Ferens K. Detecting a distributed denial of service attack using a pre-processed convolutional neural network. In electrical power and energy conference 2017 (pp. 1-6). IEEE.
[Crossref] [Google Scholar]
[8]Inoue J, Yamagata Y, Chen Y, Poskitt CM, Sun J. Anomaly detection for a water treatment system using unsupervised machine learning. In international conference on data mining workshops 2017 (pp. 1058-65). IEEE.
[Crossref] [Google Scholar]
[9]Shin J, Baek Y, Eun Y, Son SH. Intelligent sensor attack detection and identification for automotive cyber-physical systems. In symposium series on computational intelligence 2017 (pp. 1-8). IEEE.
[Crossref] [Google Scholar]
[10]Wang Y, Amin MM, Fu J, Moussa HB. A novel data analytical approach for false data injection cyber-physical attack mitigation in smart grids. IEEE Access. 2017; 5:26022-33.
[Crossref] [Google Scholar]
[11]He Y, Mendis GJ, Wei J. Real-time detection of false data injection attacks in smart grid: a deep learning-based intelligent mechanism. IEEE Transactions on Smart Grid. 2017; 8(5):2505-16.
[Crossref] [Google Scholar]
[12]Diro AA, Chilamkurti N. Distributed attack detection scheme using deep learning approach for internet of things. Future Generation Computer Systems. 2018; 82:761-8.
[Crossref] [Google Scholar]
[13]Nguyen KK, Hoang DT, Niyato D, Wang P, Nguyen D, Dutkiewicz E. Cyberattack detection in mobile cloud computing: a deep learning approach. In wireless communications and networking conference 2018 (pp. 1-6). IEEE.
[Crossref] [Google Scholar]
[14]Ghazi Z, Doustmohammadi A. Intrusion detection in cyber-physical systems based on petri net. Information Technology and Control. 2018; 47(2):220-35.
[Crossref] [Google Scholar]
[15]Niu X, Sun J. Dynamic detection of false data injection attack in smart grid using deep learning. arXiv preprint arXiv:1808.01094. 2018.
[Google Scholar]
[16]Shi D, Guo Z, Johansson KH, Shi L. Causality countermeasures for anomaly detection in cyber-physical systems. IEEE Transactions on Automatic Control. 2018; 63(2):386-401.
[Crossref] [Google Scholar]