Argumentation-Based Cyber Attribution: The DeLP3E Model

Nunes, Eric; Shakarian, Paulo; Simari, Gerardo I.; Ruef, Andrew

doi:10.1007/978-3-319-73788-1_3

Cited by 4 publications

(4 citation statements)

References 13 publications

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“…Finally, we are currently developing an implementation of LAF extending the existing DeLP system [76], which is the basis of our work 2 . The resulting implementation will be exercised in different domains that require modeling extra information associated with the arguments, taking as motivation the studies and analyses of P-DeLP [77,78], and DeLP3E [79]-DeLP has already been shown to have practical application in the cyber security somain [80].…”

Section: Discussionmentioning

confidence: 99%

An approach to characterize graded entailment of arguments through a label-based framework

Budn

Simari

Viglizzo

2017

International Journal of Approximate Reasoning

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Argumentation theory is a powerful paradigm that formalizes a type of commonsense reasoning that aims to simulate the human ability to resolve a specific problem in an intelligent manner. A classical argumentation process takes into account only the properties related to the intrinsic logical soundness of an argument in order to determine its acceptability status. However, these properties are not always the only ones that matter to establish the argument's acceptability-there exist other qualities, such as strength, weight, social votes, trust degree, relevance level, and certainty degree, among others.In this work, we redefine the argumentative process to improve the analysis of arguments by considering their special features in order to obtain more refined results. Towards this end, we propose adding meta-level information to the arguments in the form of labels representing quantifiable data ranking over a range of fuzzy valuations. These labels are propagated through an argumentative graph according to the relations of support, conflict, and aggregation between arguments. Through this process we obtain final labels that are useful in determining argument acceptability.

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Section: Discussionmentioning

confidence: 99%

An approach to characterize graded entailment of arguments through a label-based framework

Budn

Simari

Viglizzo

2017

International Journal of Approximate Reasoning

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“…The studies of Nunes et al . (2016b, c) are very interesting as they propose an argumentation model based on defeasible logic programming, designed to help the analyst find the source of a cyber-attack. These studies are based on Nunes et al .…”

Section: Argumentation and Explainable Systemsmentioning

confidence: 99%

Argumentation and explainable artificial intelligence: a survey

Vassiliades

Bassiliades

Patkos

2021

The Knowledge Engineering Review

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Argumentation and eXplainable Artificial Intelligence (XAI) are closely related, as in the recent years, Argumentation has been used for providing Explainability to AI. Argumentation can show step by step how an AI System reaches a decision; it can provide reasoning over uncertainty and can find solutions when conflicting information is faced. In this survey, we elaborate over the topics of Argumentation and XAI combined, by reviewing all the important methods and studies, as well as implementations that use Argumentation to provide Explainability in AI. More specifically, we show how Argumentation can enable Explainability for solving various types of problems in decision-making, justification of an opinion, and dialogues. Subsequently, we elaborate on how Argumentation can help in constructing explainable systems in various applications domains, such as in Medical Informatics, Law, the Semantic Web, Security, Robotics, and some general purpose systems. Finally, we present approaches that combine Machine Learning and Argumentation Theory, toward more interpretable predictive models.

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“…Non-monotonic reasoning has previously been proposed to tackle the attribution challenge. For example, in [34,43] the authors propose the DeLP3E framework to attribute operations of cyber-attacks. This theoretical framework is based on the extension of Defeasible Logic Programming with probabilistic uncertainty.…”

Section: Related Workmentioning

confidence: 99%

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

Karafili¹,

Wang²,

Lupu³

2019

Preprint

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We expect an increase in the frequency and severity of cyberattacks 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 proofof-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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Argumentation-Based Cyber Attribution: The DeLP3E Model

Cited by 4 publications

References 13 publications

An approach to characterize graded entailment of arguments through a label-based framework

An approach to characterize graded entailment of arguments through a label-based framework

Argumentation and explainable artificial intelligence: a survey

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

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