{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T10:10:36Z","timestamp":1784110236974,"version":"3.55.0"},"reference-count":50,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2021,7,22]],"date-time":"2021-07-22T00:00:00Z","timestamp":1626912000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Internet Technol."],"published-print":{"date-parts":[[2021,11,30]]},"abstract":"<jats:p>\n            Volunteer computing uses Internet-connected devices (laptops, PCs, smart devices, etc.), in which their owners volunteer them as storage and computing power resources, has become an essential mechanism for resource management in numerous applications. The growth of the volume and variety of data traffic on the Internet leads to concerns on the robustness of cyberphysical systems especially for critical infrastructures. Therefore, the implementation of an efficient Intrusion Detection System for gathering such sensory data has gained vital importance. In this article, we present a comparative study of Artificial Intelligence (AI)-driven intrusion detection systems for wirelessly connected sensors that track crucial applications. Specifically, we present an in-depth analysis of the use of machine learning, deep learning and reinforcement learning solutions to recognise intrusive behavior in the collected traffic. We evaluate the proposed mechanisms by using KDD\u201999 as real attack dataset in our simulations. Results present the performance metrics for three different IDSs, namely the Adaptively Supervised and Clustered Hybrid IDS (ASCH-IDS), Restricted Boltzmann Machine-based Clustered IDS (RBC-IDS), and Q-learning based IDS (Q-IDS), to detect malicious behaviors. We also present the performance of different reinforcement learning techniques such as State-Action-Reward-State-Action Learning (SARSA) and the Temporal Difference learning (TD). Through simulations, we show that Q-IDS performs with\n            <jats:inline-formula>\n              <jats:alternatives>\n                <jats:tex-math>\n                  \n                <\/jats:tex-math>\n              <\/jats:alternatives>\n            <\/jats:inline-formula>\n            detection rate while SARSA-IDS and TD-IDS perform at the order of\n            <jats:inline-formula>\n              <jats:alternatives>\n                <jats:tex-math>\n                  \n                <\/jats:tex-math>\n              <\/jats:alternatives>\n            <\/jats:inline-formula>\n            .\n          <\/jats:p>","DOI":"10.1145\/3406093","type":"journal-article","created":{"date-parts":[[2021,7,22]],"date-time":"2021-07-22T14:32:41Z","timestamp":1626964361000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":51,"title":["A Comparative Study of AI-Based Intrusion Detection Techniques in Critical Infrastructures"],"prefix":"10.1145","volume":"21","author":[{"given":"Safa","family":"Otoum","sequence":"first","affiliation":[{"name":"College of Technological Innovation, Zayed University, UAE"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Burak","family":"Kantarci","sequence":"additional","affiliation":[{"name":"University of Ottawa, Ottawa, ON"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hussein","family":"Mouftah","sequence":"additional","affiliation":[{"name":"University of Ottawa, Ottawa, ON"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,7,22]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.scs.2020.102080"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.2010.5601957"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2922699"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2017.7997099"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3382770"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/LSENS.2017.2752719"},{"key":"e_1_2_1_7_1","volume-title":"IEEE International Conference on Communications (ICC). 1\u20136.","author":"Otoum Safa"},{"key":"e_1_2_1_8_1","volume-title":"13th International Wireless Communications and Mobile Computing Conference (IWCMC). 153\u2013158","author":"Otoum Safa","year":"2017"},{"key":"e_1_2_1_9_1","volume-title":"International Conference on Signal and Information Processing (IConSIP). 1\u20135. http:\/\/dx.doi.org\/10","author":"Jain R.","year":"2016"},{"key":"e_1_2_1_10_1","volume-title":"24th International Conference on Telecommunications (ICT). 1\u20135. http:\/\/dx.doi.org\/10","author":"Ioannou C.","year":"2017"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.4108\/eai.3-12-2015.2262516"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2762418"},{"key":"e_1_2_1_13_1","volume-title":"2nd World Symposium on Web Applications and Networking (WSWAN). 1\u20136. http:\/\/dx.doi.org\/10","author":"Dali L.","year":"2015"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/967900.967988"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/1654988.1655002"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/AUTEST.2017.8080473"},{"key":"e_1_2_1_17_1","volume-title":"Adaptive Model Generation: An Architecture for Deployment of Data Mining-Based Intrusion Detection Systems","author":"Honig Andrew"},{"key":"e_1_2_1_18_1","volume-title":"Soft Computing in Industrial Applications, Ant\u00f3nio Gaspar-Cunha","author":"Salama Mostafa A."},{"key":"e_1_2_1_19_1","volume-title":"A Systematic Approach for the Application of Restricted Boltzmann Machines in Network Intrusion Detection","author":"Gouveia Arnaldo"},{"key":"e_1_2_1_20_1","unstructured":"Yazan Otoum Dandan Liu and Amiya Nayak. 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