Abstract-Fault tolerance in the form of diverse redundancy is well known to improve the detection rates for both malicious and non-malicious failures. What is of interest to designers of security protection systems are the actual gains in detection rates that they may give. In this paper we provide exploratory analysis of the potential gains in detection capability from using diverse AntiVirus products for the detection of self-propagating malware. The analysis is based on 1599 malware samples collected by the operation of a distributed honeypot deployment over a period of 178 days. We sent these samples to the signature engines of 32 different AntiVirus products taking advantage of the VirusTotal service. The resulting dataset allowed us to perform analysis of the effects of diversity on the detection capability of these components as well as how their detection capability evolves in time.

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Action sequences, where atomic user actions are represented in a labelled, timestamped form, are becoming a fundamental data asset in the inspection and monitoring of user behaviour in digital systems. Although the analysis of such sequences is highly critical to the investigation of activities in cyber security applications, existing solutions fail to provide a comprehensive understanding due to the complex semantic and temporal characteristics of these data. This paper presents a visual analytics approach that aims to facilitate a user-involved, multi-faceted decision making process during the identification and the investigation of unusual action sequences. We firstly report the results of the task analysis and domain characterisation process. Then we describe the components of our multi-level analysis approach that comprises of constraint-based sequential pattern mining and semantic distance based clustering, and multi-scalar visualisations of users and their sequences. Finally, we demonstrate the applicability of our approach through a case study that involves tasks requiring effective decision-making by a group of domain experts. Although our solution here is tightly informed by a user-centred, domain-focused design process, we present findings and techniques that are transferable to other applications where the analysis of such sequences is of interest.

Abstract. Rogue antivirus software has recently received extensive attention, justified by the diffusion and efficacy of its propagation. We present a longitudinal analysis of the rogue antivirus threat ecosystem, focusing on the structure and dynamics of this threat and its economics.To that end, we compiled and mined a large dataset of characteristics of rogue antivirus domains and of the servers that host them. The contributions of this paper are threefold. Firstly, we offer the first, to our knowledge, broad analysis of the infrastructure underpinning the distribution of rogue security software by tracking 6,500 malicious domains. Secondly, we show how to apply attack attribution methodologies to correlate campaigns likely to be associated to the same individuals or groups. By using these techniques, we identify 127 rogue security software campaigns comprising 4,549 domains. Finally, we contextualize our findings by comparing them to a different threat ecosystem, that of browser exploits. We underline the profound difference in the structure of the two threats, and we investigate the root causes of this difference by analyzing the economic balance of the rogue antivirus ecosystem. We track 372,096 victims over a period of 2 months and we take advantage of this information to retrieve monetization insights. While applied to a specific threat type, the methodology and the lessons learned from this work are of general applicability to develop a better understanding of the threat economies.