Review of IDS, ML and Deep Neural Network Technique in DDoS Attacks

Intrusion Detection Systems (IDS) and firewalls often struggle to identify malicious packets, creating opportunities for threat actors to exploit vulnerabilities. Advanced tactics are used by threat actors to bypass these detection mechanisms. They employ evasion techniques, such as adjusting anomalies or thresholds in anomaly-based systems and injecting ambiguity into packet data, which confuses IDS and firewalls. Despite previous applications of machine learning (ML) in cybersecurity, challenges persist. This research aims to review traditional IDS failures and examine the evolution of ML and deep neural networks (DNN) from their basic functionalities to advanced mechanisms. This study also summarizes the types of ML and DNN, along with their techniques in various applications, both individually and in combination, with a focus on detecting ICMPv4/ICMPv6 DDoS attacks and the necessity of integrating both to mitigate such attacks.