Predicting Consequences of Cyber-Attacks

Datta, Prerit; Lodinger, Natalie R.; Namin, Akbar Siami; Jones, Keith S.

doi:10.1109/bigdata50022.2020.9377825

Cited by 5 publications

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“…Explainable Users, Artificial Intelligence, Natural Language Processing, Security Attacks Comprehension, Perception, Mental Model I. INTRODUCTION One of the objectives of an ongoing project is to design a user-centric threat model by which a cyber attack is described in terms of its immediate non-technical consequences to end-users (i.e., an end-user's perspective threat model) and also to develop an interactive platform which is able to predict the consequences of cyberattacks [1]. Along this objective, the research team collected the descriptions of a number of cyber security attacks.…”

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confidence: 99%

Toward Explainable Users: Using NLP to Enable AI to Understand Users' Perceptions of Cyber Attacks

Abri¹,

Gutiérrez²,

Kulkarni³

et al. 2021

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To understand how end-users conceptualize consequences of cyber security attacks, we performed a card sorting study, a well-known technique in Cognitive Sciences, where participants were free to group the given consequences of chosen cyber attacks into as many categories as they wished using rationales they see fit. The results of the open card sorting study showed a large amount of interparticipant variation making the research team wonder how the consequences of security attacks were comprehended by the participants. As an exploration of whether it is possible to explain user's mental model and behavior through Artificial Intelligence (AI) techniques, the research team compared the card sorting data with the outputs of a number of Natural Language Processing (NLP) techniques with the goal of understanding how participants perceived and interpreted the consequences of cyber attacks written in natural languages. The results of the NLP-based exploration methods revealed an interesting observation implying that participants had mostly employed checking individual keywords in each sentence to group cyber attack consequences together and less considered the semantics behind the description of consequences of cyber attacks. The results reported in this paper are seemingly useful and important for cyber attacks comprehension from user's perspectives. To the best of our knowledge, this paper is the first introducing the use of AI techniques in explaining and modeling users' behavior and their perceptions about a context. The novel idea introduced here is about explaining users using AI.

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confidence: 99%