An Efficient Hybrid Protocol Framework for DDoS Attack Detection and Mitigation Using Evolutionary Technique

Abstract

The ever-increasing use of the Internet has created massive amounts network traffic, causing problems related to its scalability, controllability, and manageability. Sophisticated network-based denial of service (DoS) and distributed denial of service (DDoS) attacks increasingly pose a future threat. The literature proposes various methods that may help stop all HTTP DoS/DDoS assaults, but no optimal solution has been identified so far. Therefore, this paper attempts to fill the gap by proposing an alternative solution known as an efficient hybrid protocol framework for distributed DoS attack detection and mitigation (E-HPFDDM). Such an architecture addresses all aspects of these assaults by relaying on a three-layer mechanism. Layer 1 uses the outer advanced blocking (OAB) scheme which blocks unauthorized IP sources using an advanced backlisted table. Layer 2 is a validation layer that relies on the inner service trackback (IST) scheme to help determine whether the inbound request has been initiated by a legitimate or an illegitimate user. Layer 3 (inner layer) uses the deep entropy based (DEB) scheme to identify, classify and mitigate high-rate DDoS (HR-DDoS) and flash crowd (FC) attacks. The research shows that in contrast to earlier studies, the structure of the proposed system offers effective defense against DoS/DDoS assaults for web applications.

Keywords

• deep entropy based scheme
• denial of service
• distributed denial of service
• flash crowd
• high-rate DDoS
• inner service trackback