Abstract: The frequency and harmfulness of cyber-attacks are increasing every day, and with them also the amount of data that the cyber-forensics analysts need to collect and analyze. In this paper, we propose a formal analysis process that allows an analyst to filter the enormous amount of evidence collected and either identify crucial information about the attack (e.g., when it occurred, its culprit, its target) or, at the very least, perform a pre-analysis to reduce the complexity of the problem in order to then draw conclusions more swiftly and efficiently. We introduce the Evidence Logic EL for representing simple and derived pieces of evidence from different sources. We propose a procedure, based on monotonic reasoning, that rewrites the pieces of evidence with the use of tableau rules, based on relations of trust between sources and the reasoning behind the derived evidence, and yields a consistent set of pieces of evidence. As proof of concept, we apply our analysis process to a concrete cyber-forensics case study.
Connected devices will continue to grow in volume and variety. The increase of connectivity brings a drastic impact on the increase of cyber attacks. Protecting measurements are not enough, while finding who did the attack is a crucial for preventing the escalation of cyber attacks. The impact of forensics in cyber security is becoming essential for the reduction and mitigation of attacks. Forensics and attribution forensics come along with their own challenges, like the difficulties on collecting suitable evidence, and the vastness of anti-forensics tools used by the attackers to cover their traces.
The main goal of AF-Cyber is to investigate and analyse the problem of attributing cyber attacks. We plan to construct a logic-based framework for performing attribution of cyber attacks, based on cyber forensics evidence, social science approaches and an intelligent methodology for dynamic evidence collection. AF-Cyber will relieve part of the cyberattacks problem, by supporting forensics investigation and attribution with logical-based frameworks representation, reasoning and supporting tools. AF-Cyber is multi-disciplinary and collaborative, bridging forensics in cyber attacks, theoretical computer science (logics and formal proofs), security, software engineering, and social science.
AF-Cyber received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 746667.
Abstract: The increase of connectivity and the impact it has in ever day life is raising new and existing security problems that are becoming important for social good. We introduce two particular problems: cyber attack attribution and regulatory data sharing. For both problems, decisions about which rules to apply, should be taken under incomplete and context dependent information. The solution we propose is based on argumentation reasoning, that is a well suited technique for implementing decision making mechanisms under conflicting and incomplete information. Our proposal permits us to identify the attacker of a cyber attack and decide the regulation rule that should be used while using and sharing data. We illustrate our solution through concrete examples.