Next-Gen Cybersecurity: A Study on AI and Machine Learning for Enhanced Network Defense and Intrusion Detection

Abstract

This thesis explores how artiőcial intelligence (AI) can boost cybersecurity, speciőcally through next-generation őrewalls (NGFWs) and intrusion detection systems (IDS). As cyber threats become more complex, traditional security methods struggle to keep up, highlighting the need for smarter, AI-driven solutions. The research covers the basics of cybersecurity, different types of vulnerabilities and attacks, and how machine learning and deep learning can help detect and counter these threats. By reviewing the latest advancements in NGFWs and IDS, including AIenhanced őrewalls and anomaly detection, this study sheds light on how these technologies can improve network security. The importance of explainable AI (XAI) is also discussed, ensuring that these advanced systems remain transparent and trustworthy. The őndings suggest that AI has the potential to make network security more adaptable and robust against new and evolving cyber threats.