Abstract
The emergence of various smart services delivered by heterogeneous Internet of Things (IoT) devices has made daily human-life easy and comfortable. IoT devices have brought enormous convenience to various applications, no matter the IoT systems include homogeneous devices like in most sensor networks or heterogeneous devices like in smart homes or smart business applications. However, several known communication infrastructures of IoT systems are at risk to various security attacks and threats. The practice of discovering uncommon occurrences of conventional behaviors is known as anomaly detection. It is an essential tool for detecting fraud as well as network intrusion. In this work, we provide an anomaly-based model on the Extended Isolation Forest method. In our work, the available dataset ’UNSW_2018_IoT_Botnet_Final_10_best_Testing’ has been used for the experiment. Performance indicators, including accuracy, precision, recall, and F1-Score, are used to validate the performance of our suggested system. We get an Accuracy Score of 93% and F1-Score of 96% through the experiment. In addition, the most important top 12 features have a more substantial impact on correct prediction for anomaly identification and have also been identified in this study.
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Panja, S., Patowary, N., Saha, S., Nag, A. (2022). Anomaly Detection in IoT Using Extended Isolation Forest. In: Sk, A.A., Turki, T., Ghosh, T.K., Joardar, S., Barman, S. (eds) Artificial Intelligence. ISAI 2022. Communications in Computer and Information Science, vol 1695. Springer, Cham. https://doi.org/10.1007/978-3-031-22485-0_1
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