Radanliev Petar, De Roure David, Maple Carsten, Ani Uchenna
Oxford e-Research Centre, Department of Engineering Sciences, University of Oxford, Oxford, UK.
WMG Cyber Security Centre, University of Warwick, Coventry, UK.
Evol Syst (Berl). 2022;13(5):747-757. doi: 10.1007/s12530-022-09431-7. Epub 2022 Jun 4.
This article investigates cybersecurity (and risk) in the context of 'technological singularity' from artificial intelligence. The investigation constructs multiple risk forecasts that are synthesised in a new framework for counteracting risks from artificial intelligence (AI) itself. In other words, the research in this article is not just concerned with securing a system, but also analysing how the system responds when (internal and external) failure(s) and compromise(s) occur. This is an important methodological principle because not all systems can be secured, and totally securing a system is not feasible. Thus, we need to construct algorithms that will enable systems to continue operating even when parts of the system have been compromised. Furthermore, the article forecasts emerging cyber-risks from the integration of AI in cybersecurity. Based on the forecasts, the article is concentrated on creating synergies between the existing literature, the data sources identified in the survey, and forecasts. The forecasts are used to increase the feasibility of the overall research and enable the development of novel methodologies that uses AI to defend from cyber risks. The methodology is focused on addressing the risk of AI attacks, as well as to forecast the value of AI in defence and in the prevention of AI rogue devices acting independently.
The online version contains supplementary material available at 10.1007/s12530-022-09431-7.
本文在人工智能的“技术奇点”背景下研究网络安全（及风险）。该研究构建了多个风险预测，这些预测被整合到一个新框架中，以应对来自人工智能（AI）本身的风险。换句话说，本文的研究不仅关注确保系统安全，还分析系统在（内部和外部）出现故障和遭到破坏时的反应。这是一个重要的方法论原则，因为并非所有系统都能得到保护，而且完全确保一个系统的安全是不可行的。因此，我们需要构建算法，使系统即使在部分系统遭到破坏时仍能继续运行。此外，本文预测了人工智能融入网络安全所带来的新出现的网络风险。基于这些预测，本文致力于在现有文献、调查中确定的数据源和预测之间创造协同效应。这些预测用于提高整体研究的可行性，并推动开发利用人工智能抵御网络风险的新方法。该方法侧重于应对人工智能攻击的风险，以及预测人工智能在防御和防止人工智能流氓设备独立行动方面的价值。
在线版本包含可在10.1007/s12530-022-09431-7获取的补充材料。
