Scalable architecture for online prioritization of cyber threats

This paper proposes an innovative framework for the early detection of several cyber attacks, where the main component is an analytics core that gathers streams of raw data generated by network probes, builds several layer models representing different activities of internal hosts, analyzes intra-layer and inter-layer information. The online analysis of internal network activities at different levels distinguishes our approach with respect to most detection tools and algorithms focusing on separate network levels or interactions between internal and external hosts. Moreover, the integrated multi-layer analysis carried out through parallel processing reduces false positives and guarantees scalability with respect to the size of the network and the number of layers. As a further contribution, the proposed framework executes autonomous triage by assigning a risk score to each internal host. This key feature allows security experts to focus their attention on the few hosts with higher scores rather than wasting time on thousands of daily alerts and false alarms

A collaborative framework for intrusion detection in mobile networks

Abstract Mobile devices are becoming the most popular way of connection, but protocols supporting mobility represent a serious source of concerns because their initial design did not enforce strong security. This paper introduces a novel class of stealth network attacks, called mobility-based evasion, where an attacker splits a malicious payload in such a way that no part can be recognized by existing defensive mechanisms including the most modern network intrusion detection systems operating in stateful mode. We propose an original cooperative framework for intrusion detection that can prevent mobility-based evasion. The viability and performance of the proposed solution is shown through a prototype applied to Mobile IPv4, Mobile IPv6 and WiFi protocols