Biblio

The Internet of Battlefield Things (IoBT) might be one of the most expensive cyber-physical systems of the next decade, yet much research remains to develop its fundamental enablers. A challenge that distinguishes the IoBT from its civilian counterparts is resilience to a much larger spectrum of threats.

Distributed attacks originating from botnet-infected machines (bots) such as large-scale malware propagation campaigns orchestrated via spam emails can quickly affect other network infrastructures. As these attacks are made successful only by the fact that hundreds of infected machines engage in them collectively, their damage can be avoided if machines infected with a common botnet can be detected early rather than after an attack is launched. Prior studies have suggested that outgoing bot attacks are often preceded by other ``tell-tale'' malicious behaviour, such as communication with botnet controllers (C&C servers) that command botnets to carry out attacks. We postulate that observing similar behaviour occuring in a synchronised manner across multiple machines is an early indicator of a widespread infection of a single botnet, leading potentially to a large-scale, distributed attack. Intuitively, if we can detect such synchronised behaviour early enough on a few machines in the network, we can quickly contain the threat before an attack does any serious damage. In this work we present a measurement-driven analysis to validate this intuition. We empirically analyse the various stages of malicious behaviour that are observed in real botnet traffic, and carry out the first systematic study of the network behaviour that typically precedes outgoing bot attacks and is synchronised across multiple infected machines. We then implement as a proof-of-concept a set of analysers that monitor synchronisation in botnet communication to generate early infection and attack alerts. We show that with this approach, we can quickly detect nearly 80% of real-world spamming and port scanning attacks, and even demonstrate a novel capability of preventing these attacks altogether by predicting them before they are launched.

Detection of high risk network flows and high risk hosts is becoming ever more important and more challenging. In order to selectively apply deep packet inspection (DPI) one has to isolate in real time high risk network activities within a huge number of monitored network flows. To help address this problem, we propose an iterative methodology for a simultaneous assessment of risk scores for both hosts and network flows. The proposed approach measures the risk scores of hosts and flows in an interdependent manner; thus, the risk score of a flow influences the risk score of its source and destination hosts, and also the risk score of a host is evaluated by taking into account the risk scores of flows initiated by or terminated at the host. Our experimental results show that such an approach not only effective in detecting high risk hosts and flows but, when deployed in high throughput networks, is also more efficient than PageRank based algorithms.