A novel method for feature learning and network intrusion classification

Abstract With the rapid advancement in technology, network systems are becoming prone to more sophisticated types of intrusions. However, machine learning (ML) based strategies are among the most efficient and popular methods to identify the network intrusions or attacks. In this study, we examined the important and discriminative features, in order to recognize the various attacks by applying the Structural Sparse Logistic Regression (SSPLR) and Support Vector Machine (SVMs) methods. The SVMs are standard ML-based techniques, which provide the reasonable performance, however, they have few shortcomings, such as, interpretability and huge computational cost. On the other hand, the sparse modeling (SSPLR) is considered as the advanced method for the data examination and processing through regularization. The structural sparse modeling can be used to simultaneously select the distinct features or the group of discriminative features from the repository of the data set to determine the coefficient of the linear classifier, where, prior information of the feature’s structure can be mapped on various sparsity-inducing regularizations. In this way, the particular group of features yielded by the most significant network attacks are selected and potentially identified. The experiments and discussion, show that the proposed techniques have improved performance compared to the most state-of-the-art techniques, used for the Intrusion Detection System (IDS).