Detection of Cyber Attacks using Machine Learning ‎based Intrusion Detection System for IoT Based Smart ‎Cities

• Published in: EAI endorsed transactions on smart cities Vol. 7; no. 1
• Main Authors: Chohan, Maria Nawaz, Haider, Usman, Muhammad Yaseen Ayub, Hina Shoukat, Tarandeep Kaur Bhatia, Muhammad Furqan Ul Hassan
• Format: Journal Article
• Language: English
• Published: 28.06.2023
• ISSN: 2518-3893; 2518-3893
• DOI: 10.4108/eetsc.3222

The world’s dynamics is evolving with artificial intelligence (AI) and the results are smart products. A smart city has smart city is collection of smart innovations powered with AI and internet of things (IoTs). Along with the ease and comfort that the concept of a smart city pointed at, many security concerns are being raised that hinders the path of its flourishment. An Intrusion Detection System (IDS) monitors the whole network traffic and alerts in case of any anomaly. A Machine Learning-based IDS intelligently senses the network threats, takes decisions about data packet legibility and alarm the user. Researchers have deployed various ML techniques to IDS to improve the detection accuracy. This work presents a comparative analysis of various ML algorithms trained over UNSW-NB15 dataset. ADA Boost, Linear Support Vector Machine (LSVM), Auto Encoder Classifier, ‎Quadratic Support Vector Machine (QSVM) and Multi-Layer Perceptron algorithms are being employed in the stimulation. ADA Boost showed an excellent accuracy of 98.3% in the results.