Implementation Paper on Network Data Verification Using Machine Learning Classifiers Based on Reduced Feature Dimensions

Abstract

Abstract: With the rapid development of network-based applications, new risks arise and extra security mechanisms require additional attention to enhance speed and accuracy. Although many new security tools are developed, the rapid rise of malicious activity may be a major problem and therefore the ever-evolving attacks pose serious threats to network security. Network administrators rely heavily on intrusion detection systems to detect such network intrusion activity. a serious approach is machine learning methods for intrusion detection, where we learn models from data to differentiate between abnormal and normal traffic. Although machine learning methods are often used, there are some drawbacks to deep analysis of machine learning algorithms in terms of intrusion detection. during this work, we present a comprehensive analysis of some existing machine learning classifiers within the context of known intrusions into network traffic. Specifically, we analyze classification along different dimensions, that is, feature selection, sensitivity to hyper-parameter selection, and sophistication imbalance problems involved in intrusion detection. We evaluate several classifications using the NSL-KDD dataset and summarize their effectiveness using detailed experimental evaluation. Keywords: IDS, Machine Learning, Classification Algorithms, NSL-KDD Dataset, Network Intrusion Detection, Data Mining, Feature Selection, WEKA, Hyperparameters, Hyperparameter Optimization.