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Although this has led to multiple studies in the recent past, there exists a paucity of literature concerning real-time Industrial IoT attack detection. The goal of this article is to build a machine-learning approach using Industrial IoT sensor readings for accurately tracking down Industrial IoT attacks in real time. We analyze IoT system behavior under a lab-controlled series of attacks on a Secure Water Treatment (SWaT) system. The system is analytically challenging in that it results in sensor readings that resemble waveforms. To that end, we develop a novel early detection method using functional shape analysis (FSA) to extract features from the data that can capture the profile of the waveform. Our results show an efficiency-complexity trade-off between functional and non-functional methods in predicting IoT attacks.<\/jats:p>","DOI":"10.1145\/3460822","type":"journal-article","created":{"date-parts":[[2021,10,22]],"date-time":"2021-10-22T22:24:54Z","timestamp":1634941494000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["Machine Learning for Automated Industrial IoT Attack Detection: An Efficiency-Complexity Trade-off"],"prefix":"10.1145","volume":"12","author":[{"given":"Saurav","family":"Chakraborty","sequence":"first","affiliation":[{"name":"Information Systems, Analytics and Operations Department, University of Louisville, Louisville, Kentucky, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Agnieszka","family":"Onuchowska","sequence":"additional","affiliation":[{"name":"School of Information Systems and Decision Sciences, University of South Florida, Tampa, Florida, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sagar","family":"Samtani","sequence":"additional","affiliation":[{"name":"Operations and Decision Technologies, Indiana University, Bloomington, Indiana, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wolfgang","family":"Jank","sequence":"additional","affiliation":[{"name":"School of Information Systems and Decision Sciences, University of South Florida, Tampa, Florida, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brandon","family":"Wolfram","sequence":"additional","affiliation":[{"name":"School of Information Systems and Decision Sciences, University of South Florida, Tampa, Florida, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,10,22]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/MMSP.2008.4665130"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2857705.2857713"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2014.07.005"},{"key":"e_1_2_1_4_1","volume-title":"IEEE International Conference on Data Mining Workshops (ICDMW\u201918)","author":"Anton Simon Duque","year":"2018","unstructured":"Simon Duque Anton , Lia Ahrens , Daniel Fraunholz , and Hans Dieter Schotten . 2018 . 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