A novel synergistic fibroblast optimization based Kalman estimation model for forecasting time-series data

Dhivyaprabha, T T; Krishnaveni, M.; Santhi, N.; Sivanpillai, Ramesh; Jayashree, G. C.

doi:10.1007/s12530-018-9217-0

Cited by 3 publications

(1 citation statement)

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“…14 the process of self-regulation evaluating the system's resistance to cyber attacks' [27]. Evolutionary computation techniques have already been used to identify parameters, functions, estimations, and optimisations -to improve the performance of empirical scientific computing with theory time-varying data [28].…”

Section: Discussionmentioning

confidence: 99%

Super-forecasting the ‘technological singularity’ risks from artificial intelligence

et al. 2022

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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.

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Section: Discussionmentioning

confidence: 99%