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Anomaly Detection in IoT Frameworks Using Machine Learning
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Author(s): Phidahunlang Chyne (North-Eastern Hill University, India), Parag Chatterjee (National Technological University, Argentina & University of the Republic, Uruguay), Sugata Sanyal (Tata Institute of Fundamental Research, India)and Debdatta Kandar (North-Eastern Hill University, India)
Copyright: 2020
Pages: 25
Source title:
Applied Approach to Privacy and Security for the Internet of Things
Source Author(s)/Editor(s): Parag Chatterjee (National Technological University, Argentina & University of the Republic, Uruguay), Emmanuel Benoist (Bern University of Applied Sciences, Switzerland)and Asoke Nath (St. Xavier's College, Kolkata, India)
DOI: 10.4018/978-1-7998-2444-2.ch004
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Abstract
Rapid advancements in hardware programming and communication innovations have encouraged the development of internet-associated sensory devices that give perceptions and information measurements from the physical world. According to the internet of things (IoT) analytics, more than 100 IoT devices across the world connect to the internet every second, which in the coming years will sharply increase the number of IoT devices by billions. This number of IoT devices incorporates new dynamic associations and does not totally replace the devices that were purchased before yet are not utilized any longer. As an increasing number of IoT devices advance into the world, conveyed in uncontrolled, complex, and frequently hostile conditions, securing IoT frameworks displays various challenges. As per the Eclipse IoT Working Group's 2017 IoT engineer overview, security is the top worry for IoT designers. To approach the challenges in securing IoT devices, the authors propose using unsupervised machine learning model at the network/transport level for anomaly detection.
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