Effective Statistical Detection of Smart Confidentiality Attacks in Multi-Domain Networks

The need to preserve information confidentiality among network providers has prevented the actual deployment of effective Traffic Engineering (TE) solutions for QoS-enabled end-to-end connectivity services in multi-domain multi-provider networks. The use of Path Computation Element (PCE) architecture can foster the effective implementation of TE through a centralized engine devoted to end-to-end path computations. However, despite authentication, authorization and encryption, confidentiality issues may arise due to abuses of information included in path computation replies to bogus requests issued by malicious PCEs. This paper first demonstrates the security leak allowing the exposure of intra-domain information in current inter-PCE path computation procedures. Then it proposes an anomaly-based approach, namely PCE Anomaly Detector (PAD) in order to detect malicious utilization of path computation services. The proposed PAD employs a novel double-step multi-dimensional formulation based on the Sequential Hypothesis Testing (SHT) statistical classification procedure, able to recognize a suspicious sequence of requests while aiming at inferring confidential information about other domains. PAD is extensively evaluated through simulations. Results show good performance in terms of detection capabilities while guaranteeing the trade-off between accuracy and responsiveness, minimizing false alarm occurrences. Robustness against smart attacks is also proved with respect to a comprehensive set of attack patterns and under different network load conditions. Finally, intra-domain information exposition is evaluated, showing the PAD ability to preserve confidentiality.