{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T12:33:51Z","timestamp":1742387631398,"version":"3.37.3"},"reference-count":51,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,4,23]],"date-time":"2021-04-23T00:00:00Z","timestamp":1619136000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","award":["G.A. n 833955)","G.A. n 101016941"],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCP"],"abstract":"As the internet continues to be populated with new devices and emerging technologies, the attack surface grows exponentially. Technology is shifting towards a profit-driven Internet of Things market where security is an afterthought. Traditional defending approaches are no longer sufficient to detect both known and unknown attacks to high accuracy. Machine learning intrusion detection systems have proven their success in identifying unknown attacks with high precision. Nevertheless, machine learning models are also vulnerable to attacks. Adversarial examples can be used to evaluate the robustness of a designed model before it is deployed. Further, using adversarial examples is critical to creating a robust model designed for an adversarial environment. Our work evaluates both traditional machine learning and deep learning models\u2019 robustness using the Bot-IoT dataset. Our methodology included two main approaches. First, label poisoning, used to cause incorrect classification by the model. Second, the fast gradient sign method, used to evade detection measures. The experiments demonstrated that an attacker could manipulate or circumvent detection with significant probability.<\/jats:p>","DOI":"10.3390\/jcp1020014","type":"journal-article","created":{"date-parts":[[2021,4,23]],"date-time":"2021-04-23T16:08:30Z","timestamp":1619194110000},"page":"252-273","source":"Crossref","is-referenced-by-count":49,"title":["Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT"],"prefix":"10.3390","volume":"1","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5927-6026","authenticated-orcid":false,"given":"Pavlos","family":"Papadopoulos","sequence":"first","affiliation":[{"name":"Blockpass ID Lab, School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK"}]},{"given":"Oliver","family":"Thornewill von Essen","sequence":"additional","affiliation":[{"name":"Blockpass ID Lab, School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3392-9970","authenticated-orcid":false,"given":"Nikolaos","family":"Pitropakis","sequence":"additional","affiliation":[{"name":"Blockpass ID Lab, School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9817-003X","authenticated-orcid":false,"given":"Christos","family":"Chrysoulas","sequence":"additional","affiliation":[{"name":"Blockpass ID Lab, School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8819-5831","authenticated-orcid":false,"given":"Alexios","family":"Mylonas","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0809-3523","authenticated-orcid":false,"given":"William J.","family":"Buchanan","sequence":"additional","affiliation":[{"name":"Blockpass ID Lab, School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,23]]},"reference":[{"key":"ref_1","unstructured":"Sapre, S., Ahmadi, P., and Islam, K. 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