Importance of Machine Learning Algorithms to Detect Botnet DDoS Attacks
Swapna Thota, D. Menaka · 2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) · 2022
Botnet are one of the threats in a network. These Botnets can be controlled remotely by BotMaster. These Bots are keys to Importance of Machine Learning Algorithms to Detect Botnet DDoS attacks, Malwares and phishing attacks. The DDoS attacks are most dangerous malware events that disrupt whole network. To solve DDoS attacks various methods and algorithms are proposed. In that K-means algorithm is Unsupervised Learning (USML) proposed in this paper. In this paper we conduct a practically analyzing using ML algorithms i.e. K-means algorithms for the detecting Botnet DDoS attacks. For experimental analysis use UNBS-NB real-time datasets. For experimental analysis we compare K- means algorithms with Support Vector Machine (SVM), Artificial Neural network (ANN), Naive Bayes (NB) and Decision Tree (dt) for performance based comparison. In results we find K-means (USML) is showing better performance than other machine learning algorithms.