Hybrid Traffic Filtering and Anomaly Detection Model for Next-Generation Networks
Abstract
In today’s evolving digital landscape, next-generation networks(NGNs) face an expanding array of threats, including distributed denial-of-service attacks, DNS spoofing, and unauthorized data breaches. Traditional security measures that rely solely on rule-based filtering are insufficient to address these complex, dynamic attacks. In response, this paper proposes a hybrid traffic filtering and anomaly detection model that merges adaptive machine learning techniques with advanced packet inspection for real-time defense By leveraging protocols such as DSCP-based filtering to isolate malicious flows early and combining them with machine learning classifiers trained on normal network behaviors, the proposed hybrid approach can quickly spot anomalies while minimizing false positives. Empirical data from real-world applications reveals that this dual-layer strategy not only reduces packet loss but also significantly enhances detection accuracy under high-bandwidth conditions. The model’s adaptability proves effective in mitigating threats like DNS tunneling and DHCP starvation—problems that, while not inherently "new," still persist in large or specialized NGN environments. By integrating traffic filtering with anomaly detection, the approach not only blocks traditional attacks but also continuously adapts to new threat patterns—fortifying security posture. The research underscores the importance of distributed architectures and cross-organizational threat intelligence to strengthen system resilience. As NGNs continue to converge wired and wireless technologies, this integrated solution offers a scalable, future-proof methodology for ensuring robust network availability and performance.