IPCA for network anomaly detection
Athanasios Delimargas, Emmanouil Skevakis, Hassan Halabian, Ioannis Lambadaris, Nabil Seddigh, Biswajit Nandy, Rupinder Makkar · 2015
As the number, complexity and diversity of cyber threats continue to increase in network infrastructures, anomaly detection techniques constitute a crucial alternative towards enhancing network security. Principal Component Analysis (PCA) is a widely used network anomaly detection statistical methodology. Despite its ability in detecting traffic anomalies, relevant research has highlighted certain drawbacks of this technique. In our work we develop the Iterative PCA (IPCA) method to address those shortcomings. We aim at providing a useful tool that will enable a network administrator to identify network anomalies. The results of our experimentation are encouraging. They indicate that IPCA possesses promising capabilities in efficiently detecting anomalies while mitigating the limitations of the classical PCA approach.