Interpretable AI for Cyber Security: Enhancing DDoS Detection with Lime and Population-Based Training Models

Due to increasing complexity and the proliferation of cyber threats, ML-based DDoS attacks are becoming more important. DDoS attacks in which multiple attacker systems send large amounts of data to a single server or network target. It can crash applications, reduce speed, and cause significant financial and image loss. The proximity of traditional standards-based detection systems and their reliance on predefined signatures sometimes prevents them from detecting new and changing threat patterns. Distributed Denial of Service (DDoS) attacks that increase access to target servers disrupting critical infrastructure causing economic loss and cause damage to reputation Traditional detection technology products and legal systems compete to match the hallmarks of today's DDoS attacks. Machine learning (ML) provides an unprecedented new technology for adaptive visibility and intelligently analyze different levels of network traffic to detect violations. In contrast to proactive routing and regular navigation, ML-based systems offer the advantage of continuous learning and flexibility. This makes it ideal for high-speed communications. This research explores existing ML methods such as multivariate augmentation and population learning modeling (PBTLGM), which increase identification accuracy. and use interpretation tools like LIME to make the identification process more transparent and reliable for cybersecurity professionals. The results show how the combination of large datasets improves the reliability of the model. This leads to model's identification accuracy level as high as 96.8%, in addition to the raw statistics. This study underscores how important it is to emphasize that predictive AI can Use it to provide actionable insights to security teams. By combining cutting-edge ML technology, this research enables powerful, real-time solutions to stay ahead of evolving threats. And it keeps everyone connected, secure, and very stable.