Supervised detection of IoT botnet attacks

Nowadays, more and more people start using IoT devices, which raise the threats of compromising these devices, since it's easily manipulated and hacked than desktop devices. This fact increased the number of cyberattacks that relay on IoT-based Botnet attacks. In this paper, we investigate the using of a supervised technique for detecting anomalies in IoT networks. The proposed model used a random forest classifier, the training data consider only 4 types of attacks while testing considers 10 types of attacks. The proposed model was effective in detection the new attacks and achieved 99% in terms of TPR, 100% in terms of TNR, and near-zero false alarms.

[1]  Marc Dacier,et al.  A revised taxonomy for intrusion-detection systems , 2000, Ann. des Télécommunications.

[2]  Yasser Morgan,et al.  Network Intrusion Detection System using Apache Storm , 2017 .

[3]  Georgios Kambourakis,et al.  DDoS in the IoT: Mirai and Other Botnets , 2017, Computer.

[4]  Saman Taghavi Zargar,et al.  A Survey of Defense Mechanisms Against Distributed Denial of Service (DDoS) Flooding Attacks , 2013, IEEE Communications Surveys & Tutorials.

[5]  Guy Lapalme,et al.  A systematic analysis of performance measures for classification tasks , 2009, Inf. Process. Manag..

[6]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[7]  N. Altman.  An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression , 1992 .

[8]  Joel J. P. C. Rodrigues,et al.  A comprehensive survey on network anomaly detection , 2018, Telecommunication Systems.

[9]  Ahmed Rebai,et al.  Using general linear model, Bayesian Networks and Naive Bayes classifier for prediction of Karenia selliformis occurrences and blooms , 2018, Ecol. Informatics.

[10]  Zahid Akhtar,et al.  Developing an Intrusion Detection Framework for High-Speed Big Data Networks: A Comprehensive Approach , 2018, KSII Trans. Internet Inf. Syst..

[11]  Yuval Elovici,et al.  N-BaIoT—Network-Based Detection of IoT Botnet Attacks Using Deep Autoencoders , 2018, IEEE Pervasive Computing.

[12]  Hossam Faris,et al.  Unsupervised intelligent system based on one class support vector machine and Grey Wolf optimization for IoT botnet detection , 2019, Journal of Ambient Intelligence and Humanized Computing.

[13]  Jens Myrup Pedersen,et al.  An efficient flow-based botnet detection using supervised machine learning , 2014, 2014 International Conference on Computing, Networking and Communications (ICNC).