Affiliation:
1. Department of Electrical and Computer Engineering, North South University, Plot 15, Block-B, Bashundhara, Dhaka 1229, Bangladesh
Abstract
Nowadays, the Internet of Things (IoT) is a common word for the people because of its increasing number of users. Statistical results show that the users of IoT devices are dramatically increasing, and in the future, it will be to an ever-increasing extent. Because of the increasing number of users, security experts are now concerned about its security. In this research, we would like to improve the security system of IoT devices, particularly in IoT botnet, by applying various machine learning (ML) techniques. In this paper, we have set up an approach to detect botnet of IoT devices using three one-class classifier ML algorithms. The algorithms are: one-class support vector machine (OCSVM), elliptic envelope (EE), and local outlier factor (LOF). Our method is a network flow-based botnet detection technique, and we use the input packet, protocol, source port, destination port, and time as features of our algorithms. After a number of preprocessing steps, we feed the preprocessed data to our algorithms that can achieve a good precision score that is approximately 77–99%. The one-class SVM achieves the best accuracy score, approximately 99% in every dataset, and EE’s accuracy score varies from 91% to 98%; however, the LOF factor achieves lowest accuracy score that is from 77% to 99%. Our algorithms are cost-effective and provide good accuracy in short execution time.
Publisher
World Scientific Pub Co Pte Lt
Cited by
3 articles.
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1. IoT botnet attack detection using deep autoencoder and artificial neural networks;KSII Transactions on Internet and Information Systems;2023-05-31
2. An NIDS for Known and Zero-Day Anomalies;2023 19th International Conference on the Design of Reliable Communication Networks (DRCN);2023-04-17
3. One-Class Support Vector Machine with Particle Swarm Optimization for Geo-Acoustic Anomaly Detection;2021 17th International Conference on Mobility, Sensing and Networking (MSN);2021-12