Enhancing Network Security with Machine Learning-Based IDSs and IPSs: An Evaluation Using UNSW-NB15 Dataset

Detection and prevention of intrusion for computer networks are essential components that contribute to an organization's success. Machine learning is becoming a preferred method for a variety of classification and analytical issues because of recent breakthroughs in the field. Many network datasets containing pertinent and irrelevant features are available in networking communications. This raises the false alarm rate while significantly lowers the rate of intrusion detection. IDSs and IPSs have been utilizing various methodologies, and implemented to secure the availability, security, and reliability of corporate computer networks. This article examines the potential for machine learning automation in network security, a crucial area of computer networking. In 2015, dataset UNSW-NB15 was created and is the current benchmark network dataset, which is used in this article. We have implemented linear regression machine learning approach using the reduced feature space. Multiclass and binary classification are included in this paper. To compare the classifiers deployed, we calculated all of the standard evaluation parameters. The results demonstrated that accuracy with the use of binary classification 98.00% and with multiclass classification 0.01 needs further improvement or alternative methodologies to enhance the accuracy.