Remediating Anomalous Traffic Behaviour in Future Networked Environments

The diverse characteristics of network anomalies and the different recovery approaches that can subsequently be employed to remediate their effects, constitute static defence mechanisms tuned at responding to specific abnormalities suboptimal for providing an overall resilience framework. The emerging autonomic network environments in particular, require always-on, adaptive, and generic mechanisms that can integrate with the core networking infrastructure and provide for a range of self-* capabilities, ranging from selfprotection to self-tuning. In this paper we present the design and implementation of an adaptive remediation component built on top of an autonomic network node architecture [2]. A set of pluggable modules that employ diverse algorithms and explicit cross-layer interaction have been engineered to mitigate different classes of anomalous traffic behaviour in response to both legitimate and malicious external stimuli. In collaboration with an always-on measurement-based anomaly detection component, our prototype empowers the properties of self-optimisation and self-healing.