BOTNET DETECTION WITH ML TECHNIQUES USING THE BOT-IOT DATASET

Abstract

Internet of Things (IoT) gadgets have advanced quickly in the last few years, and their use is steadily rising daily. However, cyber-attackers can target these gadgets due to their distributed nature. Additionally, many IoT devices have significant security flaws in their implementation and design, making them vulnerable to security threats. Hence, these threats can result in significant data security and privacy breaches from a singular attack on network devices or systems. Botnets are a significant security risk that can harm the IoT network; hence, sophisticated techniques are required to mitigate the risk. This research employs machine learning techniques to detect IoT devices being controlled by BotNets. The proposed technique identifies the net attack by distinguishing between legitimate and malicious traffic. This article proposes a hyper parameter tuning model to improvise the method to improve the accuracy of existing processes. The results demonstrated an improved and more accurate indication of Botnet-based cyber-attacks.