Mitigating DDoS Attacks in ISP Networks through Intelligent Traffic Filtering

Abstract

Distributed Denial of Service (DDoS) attacks remain one of the most critical threats to Internet Service Providers (ISPs), with the potential to cripple core services, disrupt customer connectivity, and generate significant financial losses. This research proposes an intelligent traffic filtering framework designed for ISP scale networks, leveraging NetFlow based flow monitoring and threshold driven anomaly detection. The system dynamically identifies and suppresses abnormal traffic patterns using real time behavior modeling, integrating adaptive blacklisting and multi layer filtering at access and peering edges. By simulating multiple volumetric attack vectors including UDP flood, ICMP flood, and SYN flood on the CAIDA DDoS 2007 dataset, we evaluate system responsiveness, detection latency, and false positive rates. Experimental results indicate that early filtering at the ISP edge can reduce average downtime by 40% and maintain service continuity for unaffected users. This paper advocates for a layered, proactive defense strategy embedded within ISP infrastructure, combining protocol awareness, behavioral analytics, and automation for scalable DDoS mitigation.