4 research outputs found

    Time-stamped claim logic

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    The main objective of this paper is to define a logic for reasoning about distributed time-stamped claims. Such a logic is interesting for theoretical reasons, i.e., as a logic per se, but also because it has a number of practical applications, in particular when one needs to reason about a huge amount of pieces of evidence collected from different sources, where some of the pieces of evidence may be contradictory and some sources are considered to be more trustworthy than others. We introduce the Time-Stamped Claim Logic including a sound and complete sequent calculus. In order to show how Time-Stamped Claim Logic can be used in practice, we consider a concrete cyber-attribution case study

    An Argumentation-Based Reasoner to Assist Digital Investigation and Attribution of Cyber-Attacks

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    We expect an increase in the frequency and severity of cyber-attacks that comes along with the need for efficient security countermeasures. The process of attributing a cyber-attack helps to construct efficient and targeted mitigating and preventive security measures. In this work, we propose an argumentation-based reasoner (ABR) as a proof-of-concept tool that can help a forensics analyst during the analysis of forensic evidence and the attribution process. Given the evidence collected from a cyber-attack, our reasoner can assist the analyst during the investigation process, by helping him/her to analyze the evidence and identify who performed the attack. Furthermore, it suggests to the analyst where to focus further analyses by giving hints of the missing evidence or new investigation paths to follow. ABR is the first automatic reasoner that can combine both technical and social evidence in the analysis of a cyber-attack, and that can also cope with incomplete and conflicting information. To illustrate how ABR can assist in the analysis and attribution of cyber-attacks we have used examples of cyber-attacks and their analyses as reported in publicly available reports and online literature. We do not mean to either agree or disagree with the analyses presented therein or reach attribution conclusions
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