Dynamic Threshold Adjustment for Adaptive Traffic Filtering

Abstract

Distributed Denial of Service (DDoS) attacks pose significant challenges to network security, particularly in distinguishing between malicious traffic surges and legitimate high-traffic events such as flash crowds. Traditional static threshold-based detection systems often result in high false positive rates and service disruptions due to their inability to adapt to dynamic network conditions. This paper presents a novel Dynamic Threshold Adjustment Method for Adaptive Traffic Filtering, designed as a core component of the Integrated Adaptive Learning and Collaborative Filtering System (IAL-CFS). The proposed approach dynamically adjusts detection thresholds in real-time using a combination of traffic profiling, statistical analysis, and machine learning techniques. Key components include an adaptive threshold mechanism, context-aware filtering, and a reinforcement learning feedback loop that continuously refines system performance. The system reduces false positives, enhances detection accuracy, and ensures scalability by incorporating real-time context, such as geographic, temporal, and application-specific traffic characteristics. Through simulated and real-world traffic testing, the method demonstrates robustness against evolving attack strategies while maintaining computational efficiency. This research establishes a scalable and intelligent framework for modern DDoS mitigation, offering a significant advancement in adaptive network security solutions.