Intrusion Detection System in Software Defined Radio Networks Using Machine Learning Algorithms with Recursive Feature Elimination
Keywords:mission critical, Intrusion Detection System, Machine Learning Algorithm, Software Defined Radio Networks
The Internet has become mission vital for many businesses, colleges, and government organizations. Many people use the Internet for business, social, and personal purposes. But, behind all of this convenience and excitement, there is a dark side where "cyber criminals" try to wreak havoc by ruining our Internet-connected machines, compromising our privacy, and rendering the Internet services on which we rely inaccessible. Intrusion Detection System (IDS) provide approaches against many network attacks (e.g., DDoS attack, Ransomware attack, Botnet attack, etc.), as it blocks the harmful activities occurring in the network system. In this paper, J48, SMO, Bayes Net, Random Forest and Random Tree were utilized to identify the accuracy of the algorithms on the KDD dataset and to discover the best-suited algorithm for learning the pattern of suspicious network activity effectively. The retrieved features from the dataset were then utilized as data inputs to train the system for future intrusion behavior prediction using one of the five methods depending on the performance metrics discovered. The classification reports (Precision, Recall, and F-Measure) were generated and compared to finalize the support-validation status found throughout the testing phase of the model used in this approach.
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