Extracting inter-arrival time based behaviour from honeypot traffic using cliques

The Leurre.com project is a worldwide network of honeypot environments that collect traces of malicious Internet traffic every day. Clustering techniques have been utilized to categorize and classify honeypot activities based on several traffic features. While such clusters of traffic provide useful information about different activities that are happening in the Internet, a new correlation approach is needed to automate the discovery of refined types of activities that share common features. This paper proposes the use of packet inter-arrival time (IAT) as a main feature in grouping clusters that exhibit commonalities in their IAT distributions. Our approach utilizes the cliquing algorithm for the automatic discovery of cliques of clusters. We demonstrate the usefulness of our methodology by providing several examples of IAT cliques and a discussion of the types of activity they represent. We also give some insight into the causes of these activities. In addition, we address the limitation of our approach, through the manual extraction of what we term supercliques, and discuss ideas for further improvement

The use of packet inter-arrival times for investigating unsolicited Internet traffic

Monitoring the Internet reveals incessant activity, that has been referred to as background radiation. In this paper, we propose an original approach that makes use of packet inter-arrival times, or IATs, to analyse and identify such abnormal or unexpected network activity. Our study exploits a large set of data collected on a distributed network of honeypots during more than six months. Our main contribution in this paper is to demonstrate the usefulness of IAT analysis for network forensic purposes, and we illustrate this with examples in which we analyse particular IAT peak values. In addition, we pinpoint some network anomalies that we have been able to determine through such analysis

Cyberprints: Identifying Cyber Attackers by Feature Analysis

The problem of attributing cyber attacks is one of increasing importance. Without a solid method of demonstrating the origin of a cyber attack, any attempts to deter would-be cyber attackers are wasted. Existing methods of attribution make unfounded assumptions about the environment in which they will operate: omniscience (the ability to gather, store, and analyze any data relevant to an attack), omnipresence (the ability to place sensors wherever necessary regardless of jurisdiction or ownership), and \emph{a priori} positioning (ignorance of the real costs of placing sensors in speculative locations). The reality is that attribution must be able to occur with only the information available directly to a forensic analyst, gathered within the target network, using budget-conscious placement of sensors and analyzers. These assumptions require a new form of attribution. This work evaluates the use of a number of network-level features as an analog of stylistic markers in literature. We find that principal component analysis is not a useful tool in analyzing these features. We are, however, able to perform Kolmogorov-Smirnov comparisons upon the feature set distributions directly to find a subset of the examined features which hold promise for forming the foundation of a \emph{Cyberprint}. This foundation could be used to examine other potential features for discriminatory power, and to establish a new direction for network forensic analysis

An examination of the Asus WL-HDD 2.5 as a nepenthes malware collector

The Linksys WRT54g has been used as a host for network forensics tools for instance Snort for a long period of time. Whilst large corporations are already utilising network forensic tools, this paper demonstrates that it is quite feasible for a non-security specialist to track and capture malicious network traffic. This paper introduces the Asus Wireless Hard disk as a replacement for the popular Linksys WRT54g. Firstly, the Linksys router will be introduced detailing some of the research that was undertaken on the device over the years amongst the security community. It then briefly discusses malicious software and the impact this may have for a home user. The paper then outlines the trivial steps in setting up Nepenthes 0.1.7 (a malware collector) for the Asus WL-HDD 2.5 according to the Nepenthes and tests the feasibility of running the malware collector on the selected device. The paper then concludes on discussing the limitations of the device when attempting to execute Nepenthes