Filtration Model For DDoS Attack Detection in Real-Time - Ahmed, Abdulghani Ali (Penerbit UMP, 2015)
Filtering traffic of distributed denial of services (DDoS) attack requires extra overhead which mostly results in network performance degradation. This study proposes a filtration model for detecting DDoS attack in real-time without causing negative degradation against network performance. The model investigates network traffic in a scalable way to detect user violations on quality of service regulations. Traffic investigation is triggered only when the network is congested; at that exact moment, burst gateways actually generate an explicit congestion notification to misbehaving users. The misbehaving users are thus further investigated by measuring their consumption ratios of bandwidth. By exceeding the service level agreement bandwidth ratio, user traffic is filtered as malicious traffic. Simulation results demonstrate that the proposed model efficiently monitors malicious traffic and precisely detects DDoS attack.