نمایش مختصر رکورد

dc.contributor.authorSalehi, Amirhoseinen_US
dc.contributor.authorAhmadi, Siavashen_US
dc.contributor.authorAref, Mohammad Rezaen_US
dc.date.accessioned1402-08-26T19:52:11Zfa_IR
dc.date.accessioned2023-11-17T19:52:13Z
dc.date.available1402-08-26T19:52:11Zfa_IR
dc.date.available2023-11-17T19:52:13Z
dc.date.issued2023-10-01en_US
dc.date.issued1402-07-09fa_IR
dc.date.submitted2023-10-18en_US
dc.date.submitted1402-07-26fa_IR
dc.identifier.citationSalehi, Amirhosein, Ahmadi, Siavash, Aref, Mohammad Reza. (2023). A Semi-Supervised IDS for Cyber-Physical Systems Using a Deep Learning Approach. The ISC International Journal of Information Security, 15(3)doi: 10.22042/isecure.2023.181544en_US
dc.identifier.issn2008-2045
dc.identifier.issn2008-3076
dc.identifier.urihttps://dx.doi.org/10.22042/isecure.2023.181544
dc.identifier.urihttps://www.isecure-journal.com/article_181544.html
dc.identifier.urihttps://iranjournals.nlai.ir/handle/123456789/1047855
dc.description.abstractIndustrial control systems are widely used in industrial sectors and critical infrastructures to monitor and control industrial processes. Recently, the security of industrial control systems has attracted a lot of attention, because these systems are now increasingly interacting with the Internet. Classic systems are suffering from many security problems and with the expansionof Internet connectivity, they are now exposed to new types of threats and cyber-attacks. Addressing this, intrusion detection technology is one of the most important security solutions that is used in industrial control systems to identifypotential attacks and malicious activities. In this paper, we propose Stacked Autoencoder-Deep Neural Network (SAE-DNN), as a semi-supervised Intrusion Detection System (IDS) with appropriate performance and applicability on a wide range of Cyber-Physical Systems (CPSs). The proposed approach comprises a stacked autoencoder, a deep learning-based feature extractor, helping us with a low dimension and low noise representation of data. In addition, our system includes a deep neural network (DNN)-based classifier, which is used to detect anomalies with a high detection rate and low false positive rate in a real-time process. The SAE-DNN's performance is evaluated on the WADI dataset, which is a real testbed for a water distribution system. The results indicate the superior performance of our approach over existing supervised and unsupervised methods while using a few percentages of labeled data.en_US
dc.format.extent1244
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoen_US
dc.publisherIranian Society of Cryptologyen_US
dc.relation.ispartofThe ISC International Journal of Information Securityen_US
dc.relation.isversionofhttps://dx.doi.org/10.22042/isecure.2023.181544
dc.subjectAutoencoderen_US
dc.subjectCyber-attacken_US
dc.subjectIndustrial Control Systemsen_US
dc.subjectIntrusion Detection Systemen_US
dc.subjectDeep Learningen_US
dc.titleA Semi-Supervised IDS for Cyber-Physical Systems Using a Deep Learning Approachen_US
dc.typeTexten_US
dc.typeResearch Articleen_US
dc.contributor.departmentInformation Systems and Security Lab, Department of Electrical Engineering, Sharif University of Technology, Tehran, Iranen_US
dc.contributor.departmentElectronics Research Institute, Sharif University of Technology, Tehran, Iranen_US
dc.contributor.departmentInformation Systems and Security Lab, Department of Electrical Engineering, Sharif University of Technology, Tehran, Iranen_US
dc.citation.volume15
dc.citation.issue3


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