Review 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 additional security mechanisms require additional attention to improve speed and accuracy. Although many new security tools have been developed, the rapid rise of malicious activity is a serious problem and 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 major 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 shortcomings in the in-depth analysis of machine learning algorithms in terms of intrusion detection. In this work, we present a comprehensive analysis of some existing machine learning classifiers with respect to known intrusions into network traffic. Specifically, we analyze classification with different dimensions, that is, feature selection, sensitivity to hyper-parameter selection, and class imbalance problems that are 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.