Analyzing Temporal Dependency Structures in Cyber security: A Case Study of Network Traffic Anomalies
DOI:
https://doi.org/10.54536/ajdsai.v2i1.5081Keywords:
Cyber Security, Network Monitoring, Networks Metrics, Time Series Analysis, Traffic DataAbstract
This research dives deep into the intricate world of temporal dependency structures found in network traffic data, specifically for cybersecurity applications. Investigation of how understanding the timing and relationships between various network metrics can significantly boost our ability to detect anomalies in cyber security systems were carried out. By employing advanced multivariate time series analysis techniques like Vector Auto regression (VAR), Dynamic Bayesian Networks (DBNs), and cutting-edge deep learning methods, which show that taking these temporal dependencies into account leads to a marked improvement in spotting sophisticated attacks, especially when compared to traditional univariate approaches. The ensemble model boasts an impressive 94.3% precision and 91.7% recall in identifying network anomalies across a range of attack vectors, such as distributed denial-of-service (DDoS) attacks, port scanning activities, and data exfiltration attempts. These findings highlight that grasping the complex temporal relationships among network metrics offers vital insights into network behavior patterns, which can be harnessed to create more resilient cyber security monitoring systems.
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Copyright (c) 2026 Chioma C. Howard, Firstman N. Otobo, Eyinanabo Odogu
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