Machine Learning for Botnet Detection: An Optimized Feature Selection Approach

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2021-12Rights
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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.Version
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Lefoane M, Ghafir I, Kabir S and Awan IU (2021) Machine Learning for Botnet Detection: An Optimized Feature Selection Approach. The 5th International Conference on Future Networks & Distributed Systems (ICFNDS 2021). December 15–16, 2021. Dubai, United Arab Emirates. ACM, New York. 6 pages.Link to Version of Record
https://doi.org/10.1145/3508072.3508102Type
Conference paperae974a485f413a2113503eed53cd6c53
https://doi.org/10.1145/3508072.3508102