Advanced Machine Learning Boosting Strategies for Improved Intrusion Detection Performance

Abstract In the rapidly evolving digital landscape, the increased utilization of networks has given rise to numerous security challenges. With the integration of the digital world into society, the emergence of new threats such as viruses and worms has become prevalent. Malicious actors employ various techniques, including password cracking and detecting unencrypted text, to exploit vulnerabilities within computer systems. Consequently, users must prioritize security measures to safeguard their systems against unauthorized intrusions. One well-established method for protecting private networks from external threats is the firewall technique. Firewalls serve as a protective barrier by filtering incoming Internet traffic. However, certain access methods, such as connecting to the Intranet via a modem within the private network, can evade detection by conventional firewalls. To address this issue, a novel system known as a Network Intrusion Detection System (IDS) has been developed to effectively identify and mitigate network attacks. In this project, an Intrusion Detection System utilizing Machine Learning has been developed to accurately determine the presence of intrusions. Multiple models have been constructed using sklearn and ensemble techniques, resulting in exceptional accuracy. This system serves as a proactive approach to bolster network security and confront the constantly developing spectrum of cyber-attacks.