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Title	Boost-Defence For Resilient Iot Networks: A Head-To-Toe Approach
ID_Doc	12772
Authors	Abu Al-Haija Q.; Al Badawi A.; Bojja G.R.
Year	2022
Published	Expert Systems, 39, 10
DOI	http://dx.doi.org/10.1111/exsy.12934
Abstract	The Internet of Things (IoT) is an emerging technology that is considered a key enabler for next-generation smart cities, industries, security services and economies. IoT networks allow connected devices to communicate with each other automatically without human intervention which empowers innovative solutions for pressing challenges and limitations of current technologies required to materialize smart environments. Due to the concrete involvement of IoT networks in critical infrastructures and cyber-physical systems, defending them against cyber-attacks has led to extensive research efforts to propose effective countermeasures against such attacks. In this work, we present Boost-Defence: a framework to secure IoT networks from a large vector of cyber-attacks at different IoT layers. We employ the AdaBoost machine learning technique combined with Decision Trees and extensive data engineering techniques to construct a robust classifier for detecting and classifying several cyber-attacks in IoT networks. We evaluate our system on the TON_IoT_2020 datasets, a collection of datasets compiled specifically for 3-layered IoT systems comprising: physical, network and application layers. We contrast the performance of our system against existing state-of-the-art solutions. Our experimental analysis demonstrates the capability of our framework in providing superior classification accuracy and lower types 1 and 2 errors for constructing more resilient IoT infrastructures. © 2022 John Wiley & Sons Ltd.
Author Keywords	classification methods; cyber security; internet of things; intrusion detection systems; machine learning; supervised learning

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