Intrusion Type Classifier: A Machine Learning Based Approach for Automated Detection and Classification of Network Intrusions

The increased complexity and frequency of network breaches present substantial obstacles in network system security maintenance. In order to tackle this issue, a proposed solution is the implementation of an Intrusion Type Classifier (ITC), which utilizes a machine learning methodology to identify and categorize network intrusions autonomously. The system employs a wide range of variables derived from network traffic data and employs multiple machine learning methods to effectively identify and classify various forms of intrusions. The primary objective of the ITC is to improve the effectiveness and precision of intrusion detection through the utilization of a multi-class classification framework. This framework is designed to differentiate between different types of intrusions, including Denial of Service (DoS), Distributed Denial of Service (DDoS), intrusions caused by malware, and other forms of network attacks. Through the utilization of extensive datasets, the ITC exhibits encouraging outcomes in the identification and classification of these intrusions, achieving notable levels of precision and recall rates. The presented methodology demonstrates a pragmatic and efficient strategy for enhancing the security of computer networks, facilitating prompt reactions to possible risks and weaknesses in intricate network settings. The support vector machine algorithm is widely recognized as one of the top-performing algorithms in various Network infrastructure domains.