Network Intrusion Detection by Optimize Feature Engineering Using Hybridization of GWO and Nonlinear Activation Function
Keywords:
Intrusion detection, Machine LearningAbstract
Social networks, connections with others, and a revolution in our lives have all been made possible by the internet. Sharing business and personal information, however, puts people and organizations at risk. An important problem is the security of data, and intrusion detection systems (IDS) are essential for shielding users from malevolent network attacks. Traditional rule-based systems find it difficult to adjust to evolving cyber threats. Techniques for machine learning (ML) have become a practical way to increase the efficacy and efficiency of intrusion detection. A thorough understanding of machine learning (ML)--based intrusion detection is heavily sought after by researchers and practitioners who want to build stronger, more effective defenses against cyber attacks. This research improves the class imbalance problem in the KDD-99 dataset by optimizing non-linear feature weights using Grey Wolves and activating Leaky RElu with a back propagation technique. Weighted features boost feature information while reducing noise across all features. During experiment analysis, class-wise accuracy is represented by a confusion matrix and an ROC curve for complete performance analysis. In terms of results, accuracy improves by 2-3%, precision by 3-4%, and precise recall improves by 5-6% on average across classes. In the experiment, a class imbalance in three classes improved by 3-4%.
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