Short Paper: Towards An Edge-Located Time-Series Database

Krentz, Timothy; Dubey, Abhishek; Karsai, Gábor

doi:10.1109/isorc.2019.00037

Cited by 4 publications

(4 citation statements)

References 4 publications

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“…This would involve constructing a scenario to teach the algorithm how adversarial systems operate and how to build systems that will prevent such scenarios from happening. To identify training data for the second algorithm we need to expand the search in new and emerging of data, open -Open Data Institute, Elgin, DataViva; spatiotemporal data -GeoBrick [12], Urban Flow prediction [13], Air quality [14], GIS platform [15]; high-dimensional data -Industrial big data [16], IGA-ELM [17], MDS [18], TMAP [19]; time-stamped data -Qubit, Edge MWN [20], Mobi-IoST [21], Edge DHT analytics [22]; real-time data -CUSUM [23], and big data [24].…”

Section: Solutions For Enhancing Cybersecurity Aimentioning

confidence: 99%

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

Radanliev

Roure

Maple³

et al. 2023

Preprint

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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 AI itself. In other words, the research concern in this article is not just with securing a system, but to analyse how the system responds when (internally and externally caused) failure and compromise occur. This is an important methodological principle because not all systems can be secured, and 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 artificial intelligence 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 methodology that uses AI to defend from cyber risk. The methodology is focused on addressing the risk of AI attack, 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: Solutions For Enhancing Cybersecurity Aimentioning

confidence: 99%

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

Radanliev

Roure

Maple³

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…Time-series databases require data storage that can serve the execution of smart infrastructure queries, but cloud-based time-series storage can be expensive. With the increasing computing power and memory in connected devices, time-series data storage and analytics can be moved to the edge with distributed hash tables (DHT) [48]. Edge computing enables different types of predictive analytics, including big data driven predictive catching in mobile wireless networks (MWN) at the wireless edge [10].…”

Section: Time-stamped Datamentioning

confidence: 99%

New and emerging forms of data and technologies: literature and bibliometric review

Radanliev

Roure

2022

Multimed Tools Appl

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With the increased digitalisation of our society, new and emerging forms of data present new values and opportunities for improved data driven multimedia services, or even new solutions for managing future global pandemics (i.e., Disease X). This article conducts a literature review and bibliometric analysis of existing research records on new and emerging forms of multimedia data. The literature review engages with qualitative search of the most prominent journal and conference publications on this topic. The bibliometric analysis engages with statistical software (i.e. R) analysis of Web of Science data records. The results are somewhat unexpected. Despite the special relationship between the US and the UK, there is not much evidence of collaboration in research on this topic. Similarly, despite the negative media publicity on the current relationship between the US and China (and the US sanctions on China), the research on this topic seems to be growing strong. However, it would be interesting to repeat this exercise after a few years and compare the results. It is possible that the effect of the current US sanctions on China has not taken its full effect yet.

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“…This would involve constructing a scenario to teach the algorithm how adversarial systems operate and how to build systems that will prevent such scenarios from happening. To identify training data for the second algorithm we need to expand the search in new and emerging forms of data, e.g., open data -Open Data Institute 1 , Elgin 2 , DataViva 3 ; spatiotemporal data -GeoBrick [12], Urban Flow prediction [13], Air quality [14], GIS platform [15]; high-dimensional data -Industrial big data [16], IGA-ELM [17], MDS [18], TMAP [19]; time-stamped data -Qubit 4 , Edge MWN [20], Mobi-IoST [21], Edge DHT analytics [22]; real-time data -CUSUM [23], and big data [24].…”

Section: Solutions For Enhancing Cybersecurity With Aimentioning

confidence: 99%

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

et al. 2022

View full text Add to dashboard Cite

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.

show abstract

Short Paper: Towards An Edge-Located Time-Series Database

Cited by 4 publications

References 4 publications

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

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

New and emerging forms of data and technologies: literature and bibliometric review

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

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