Analysis of Machine Learning Algorithms for IoT Botnet

The Internet of Things (IoT) gains a lot of popularity day-by-day due to their everlasting availability and ease. As the popularity of IoT increases, it also attracts hackers which try to take advantage of the vulnerability of IoT devices. An Intrusion Detection System (IDS) is an intelligence-based system that can investigate or detect the intrusion in the IoT botnet and check the state of software and hardware executing in the network. Once the intrusion is detected, it may generate an alarm to alert the administrator or send some alert message to the owner. In the last decade, there are several IDSs available which can detect the intrusion. But the major problems with the existing IDSs like accuracy rate, generation of the false alarm, and fewer chances of detection of unknown attacks. To deal with the above problems, some machine learning techniques have been involved by researchers. These techniques can differentiate between the normal and abnormal behavior of the user's data or network traffic with high accuracy. In this paper, we summarize and classify the machine learning algorithms that can be used in IDS with their metrics, parameters. Then, a case study is implemented with the UNSW-NB15 dataset that has realistic network traffic with frequently used machine learning techniques. After that, a comparison will be done and displayed by using an accuracy percentage table and a bar chart. Finally, some challenges and future scope of the machine learning techniques in the improvement of IDS will be discussed.