Deep Learning Model-Based Approach for DDoS Detection and Classification

The prevalence of cyber vulnerabilities in our interconnected world has led to a constant barrage of attacks targeting individuals and organizations. Among these assaults, DDoS threats loom as a major concern. DDoS attacks are unique in their ability to disrupt Internet services without physical access or system modification. These assaults inundate networks with fake traffic, causing downtime, service interruptions, financial losses, reputation damage, and even complete network shutdown. Traditional DDoS detection methods have struggled to navigate the diverse landscape of attack types. They often exhibit limited sensitivity and high false positive rates, hindering accurate identification and mitigation of DDoS attacks. In contrast, emerging deep learning techniques provide a promising alternative. They excel in classifying and detecting these attacks early, enabling organizations to establish more effective defenses against DDoS threats. Data, which has grown increasingly complex, plays a pivotal role in countering DDoS attacks. Existing intrusion detection system (IDS) models designed for DDoS detection suffer from significant drawbacks, including latency issues, lengthy integration times, and the challenge of identifying both global and local optimal trap solutions. These issues find efficient solutions in recommended deep learning and neural network-based algorithms. These models leverage hidden neurons in their initial stages to improve DDoS attack detection’s precision and effectiveness. Our research utilized deep learning models and neural networks to identify DDoS attacks. Additionally, our proposed optimization approach intelligently selects the most critical features, leading to substantial improvements in detection accuracy.