{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T01:17:56Z","timestamp":1774315076770,"version":"3.50.1"},"reference-count":25,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,7,20]],"date-time":"2020-07-20T00:00:00Z","timestamp":1595203200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>In today\u2019s digital world, the information systems are revolutionizing the way we connect. As the people are trying to adopt and integrate intelligent systems into daily lives, the risks around cyberattacks on user-specific information have significantly grown. To ensure safe communication, the Intrusion Detection Systems (IDS) were developed often by using machine learning (ML) algorithms that have the unique ability to detect malware against network security violations. Recently, it was reported that the IDS are prone to carefully crafted perturbations known as adversaries. With the aim to understand the impact of such attacks, in this paper, we have proposed a novel random neural network-based adversarial intrusion detection system (RNN-ADV). The NSL-KDD dataset is utilized for training. For adversarial attack crafting, the Jacobian Saliency Map Attack (JSMA) algorithm is used, which identifies the feature which can cause maximum change to the benign samples with minimum added perturbation. To check the effectiveness of the proposed adversarial scheme, the results are compared with a deep neural network which indicates that RNN-ADV performs better in terms of accuracy, precision, recall, F1 score and training epochs.<\/jats:p>","DOI":"10.3390\/computers9030058","type":"journal-article","created":{"date-parts":[[2020,7,20]],"date-time":"2020-07-20T10:59:38Z","timestamp":1595242778000},"page":"58","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["An Adversarial Approach for Intrusion Detection Systems Using Jacobian Saliency Map Attacks (JSMA) Algorithm"],"prefix":"10.3390","volume":"9","author":[{"given":"Ayyaz Ul Haq","family":"Qureshi","sequence":"first","affiliation":[{"name":"School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6826-207X","authenticated-orcid":false,"given":"Hadi","family":"Larijani","sequence":"additional","affiliation":[{"name":"School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mehdi","family":"Yousefi","sequence":"additional","affiliation":[{"name":"School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahsan","family":"Adeel","sequence":"additional","affiliation":[{"name":"deepCI.org, Edinburgh EH16 5XW, UK"},{"name":"School of Mathematics and Computer Science, University of Wolverhampton, Wolverhampton WV1 1LY, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nhamoinesu","family":"Mtetwa","sequence":"additional","affiliation":[{"name":"Barclays Bank Plc., Glasgow G2 7JT, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ferdowsi, A., and Saad, W. 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