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Title	Machine Learning For Botnet Detection: An Optimized Feature Selection Approach
ID_Doc	35958
Authors	Lefoane M.; Ghafir I.; Kabir S.; Awan I.-U.
Year	2021
Published	ACM International Conference Proceeding Series
DOI	http://dx.doi.org/10.1145/3508072.3508102
Abstract	Technological advancements have been evolving for so long, particularly Internet of Things (IoT) technology that has seen an increase in the number of connected devices surpass non IoT connections. It has unlocked a lot of potential across different organisational settings from healthcare, transportation, smart cities etc. Unfortunately, these advancements also mean that cybercriminals are constantly seeking new ways of exploiting vulnerabilities for malicious and illegal activities. IoT is a technology that presents a golden opportunity for botnet attacks that take advantage of a large number of IoT devices and use them to launch more powerful and sophisticated attacks such as Distributed Denial of Service (DDoS) attacks. This calls for more research geared towards the detection and mitigation of botnet attacks in IoT systems. This paper proposes a feature selection approach that identifies and removes less influential features as part of botnet attack detection method. The feature selection is based on the frequency of occurrence of the value counts in each of the features with respect to total instances. The effectiveness of the proposed approach is tested and evaluated on a standard IoT dataset. The results reveal that the proposed feature selection approach has improved the performance of the botnet attack detection method, in terms of True Positive Rate (TPR) and False Positive Rate (FPR). The proposed methodology provides 100% TPR, 0% FPR and 99.9976% F-score. © 2021 ACM.
Author Keywords	Botnet Detection; Cyber Attacks; Internet of Things; Machine Learning; Network security

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