Naveen Reddy Burramukku
The increasing complexity of modern network infrastructures, driven by cloud computing, Internet of Things (IoT), and high-speed data transmission, has made real-time network threat detection a critical challenge. Traditional security mechanisms such as signature-based intrusion detection systems and standalone anomaly detection techniques often fail to identify sophisticated and zero-day attacks in real time. These systems either lack contextual awareness or are unable to scale effectively under high traffic volumes. To address these challenges, this research proposes a hybrid real-time network threat detection framework that integrates Deep Packet Inspection (DPI) with telemetry analytics. Deep Packet Inspection provides fine-grained visibility into packet headers and payloads, enabling the identification of malicious patterns, protocol violations, and payload-based threats. However, DPI alone is computationally expensive and limited in its ability to analyze encrypted traffic. Telemetry analytics, on the other hand, offers scalable and efficient monitoring by analyzing flow-level and behavioral data collected from network devices. While telemetry-based systems are effective in identifying anomalous traffic patterns, they often lack the contextual depth required to accurately classify threats, resulting in high false-positive rates. The proposed framework combines the strengths of DPI and telemetry analytics by correlating packet-level features with flow-level behavioral metrics in a unified real-time detection pipeline. The system captures network traffic simultaneously at multiple layers, extracts relevant features, and applies analytics techniques to detect malicious activities such as intrusion attempts, malware communication, data exfiltration, and distributed denial-of-service (DDoS) attacks. A real-time processing engine ensures low detection latency, making the framework suitable for deployment in high-speed enterprise and cloud networks. Experimental evaluation using a mixed dataset of benign and malicious traffic demonstrates that the hybrid approach significantly improves detection accuracy, reduces false positives, and achieves faster response times compared to DPI-only and telemetry-only detection systems. The results highlight the effectiveness of integrating packet inspection with network telemetry for enhanced situational awareness and proactive threat detection. This research contributes a scalable and practical solution for next-generation network security systems.
Network Security, Deep Packet Inspection, Telemetry Analytics, Real-Time Threat Detection, Intrusion Detection Systems, Cybersecurity Analytics