Dynamic AI-Augmented Firewall for Real-Time Threat Mitigation

Abstract

Firewalls are an integral part of network protection against intrusions, their traditional approaches of sticking to static rules have rendered firewalls ineffective when countering sophisticated cyber threats. In this project, a Dynamic AI-Augmented Firewall will be developed which uses artificial intelligence augmented firewall for the proactive detection and response to cyber threats thereby undertaking network protection and security better than traditional firewalls. To accomplish the objectives stated above, the study will develop an AI-enabled anomaly detection system that will constantly scan for abnormal network traffic and manage firewall policy changes and restrictions whenever an abnormal algorithmic pattern is noticed. The methodological approach will encompass securing the network by employing machine learning algorithms fed with global threat intelligence to keep the network safe from new attacks. Pre-packaged threats that are already established and documented have also been proved less active. Promising conclusions show that an AI-Augmented Firewall is very promising with regards to its low false positive which comes with little additional network delay. The report concludes that to provide a scalable solution that increases the resilience of an organization against advanced threats, it is very important that an organization adopts adaptive security measures. This work fits into the growing field of cybersecurity by providing proof of the rigour of AI applications on firewalls and calls for more robust means of defense against intrusions due to changing workplace dynamics.