{"title":"The Age of fighting machines: the use of cyber deception for Adversarial Artificial Intelligence in Cyber Defence","authors":"David Lopes Antunes, Salvador Llopis Sanchez","doi":"10.1145/3600160.3605077","DOIUrl":null,"url":null,"abstract":"Cyber deception has emerged as a valuable technique in the field of cybersecurity, closely linked with adversarial Artificial Intelligence. In an era of pervasive automation, it is getting prominence as a research topic aimed at understanding how novel machine learning algorithms can be deceived using adversarial attacks that exploit vulnerabilities of their models. To this end, the paper describes the state-of-the-art of cyber deception for adversarial AI purposes, focusing on its benefits, challenges, and advanced techniques. In addition, this exploratory research attempts to extend its applicability to the fact that an appropriate and timely discovery of adversarial plans and associated actions may enhance own cyber resilience by introducing analytical findings of the adversary's intent into decision-making for cyber situational awareness. The study of adversarial thinking is as old as history and is one of the most relevant subjects rapidly incorporated into the operational planning process – a methodology to understand the operational environment. Adversarial knowledge is used for adapting own cyber defences in response to the cyber threat landscape.","PeriodicalId":107145,"journal":{"name":"Proceedings of the 18th International Conference on Availability, Reliability and Security","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 18th International Conference on Availability, Reliability and Security","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3600160.3605077","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Cyber deception has emerged as a valuable technique in the field of cybersecurity, closely linked with adversarial Artificial Intelligence. In an era of pervasive automation, it is getting prominence as a research topic aimed at understanding how novel machine learning algorithms can be deceived using adversarial attacks that exploit vulnerabilities of their models. To this end, the paper describes the state-of-the-art of cyber deception for adversarial AI purposes, focusing on its benefits, challenges, and advanced techniques. In addition, this exploratory research attempts to extend its applicability to the fact that an appropriate and timely discovery of adversarial plans and associated actions may enhance own cyber resilience by introducing analytical findings of the adversary's intent into decision-making for cyber situational awareness. The study of adversarial thinking is as old as history and is one of the most relevant subjects rapidly incorporated into the operational planning process – a methodology to understand the operational environment. Adversarial knowledge is used for adapting own cyber defences in response to the cyber threat landscape.