Improving Network Security Using Intelligent Ensemble Techniques: an Integrated System for Detecting and Managing Intrusions in Computer Networks

Increasing security measures to prevent data breaches and network invasions has grown more crucial due to the Internet's and networks' exponential growth. Since regular and intrusion traffic is so similar, it can be difficult for traditional firewalls to detect and block intrusions, especially when they are embedded in network packets. Numerous existing algorithms and monitoring techniques are made more complex by the sheer volume of network traffic. Diverse strategies for intrusion detection have been put out in response to these difficulties, with machine learning techniques emerging as a potentially effective answer. Taking advantage of machine learning's capabilities, this study presents a novel ensemble learning paradigm-based pattern recognition technique intended for network intrusion detection. Through the utilization of several learning models together, the ensemble technique improves the system's capacity to identify traces of intrusions in the midst of a large volume of complex network data. Not only does the study suggest this sophisticated approach, but it also lists open issues, pointing out possible directions for development, and investigates data fusion options, underscoring the complexity of dealing with the changing network security environment.