Dynamic real-time risk analytics of uncontrollable states in complex internet of things systems: cyber risk at the edge

Radanliev, Petar; Roure, David De; Kleek, Max Van; Ani, Uchenna; Burnap, Pete; Anthi, Eirini; Nurse, Jason R. C.; Santos, Omar; Montalvo, Rafael Mantilla; Maddox, La’Treall

doi:10.1007/s10669-020-09792-x

Cited by 7 publications

(3 citation statements)

References 35 publications

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“…Therefore, our methodology is based on mathematical principles and quantitative data. In recent publications on this topic (Radanliev et al 2020b), we discovered that the lack of probabilistic data leads to qualitative cyber risk assessment approaches, where the outcome represents a speculative assumption. Emerging quantitative models are effectively designed with ranges and confidence intervals based on expert opinions and not probabilistic data (Buith 2016).…”

Section: Methodsmentioning

confidence: 99%

Design of a dynamic and self-adapting system, supported with artificial intelligence, machine learning and real-time intelligence for predictive cyber risk analytics in extreme environments – cyber risk in the colonisation of Mars

Radanliev

Roure

Page

et al. 2020

Saf. Extreme Environ.

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Multiple governmental agencies and private organisations have made commitments for the colonisation of Mars. Such colonisation requires complex systems and infrastructure that could be very costly to repair or replace in cases of cyber-attacks. This paper surveys deep learning algorithms, IoT cyber security and risk models, and established mathematical formulas to identify the best approach for developing a dynamic and self-adapting system for predictive cyber risk analytics supported with Artificial Intelligence and Machine Learning and real-time intelligence in edge computing. The paper presents a new mathematical approach for integrating concepts for cognition engine design, edge computing and Artificial Intelligence and Machine Learning to automate anomaly detection. This engine instigates a step change by applying Artificial Intelligence and Machine Learning embedded at the edge of IoT networks, to deliver safe and functional real-time intelligence for predictive cyber risk analytics. This will enhance capacities for risk analytics and assists in the creation of a comprehensive and systematic understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when Artificial Intelligence and Machine Learning technologies are migrated to the periphery of the internet and into local IoT networks.

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Section: Methodsmentioning

confidence: 99%

Design of a dynamic and self-adapting system, supported with artificial intelligence, machine learning and real-time intelligence for predictive cyber risk analytics in extreme environments – cyber risk in the colonisation of Mars

Radanliev

Roure

Page

et al. 2020

Saf. Extreme Environ.

Self Cite

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“…It also increases the threat posed by cyberattacks such as system compromise, service interruption, and information leakage [5,6]. In particular, the possibility of cyberattacks on drones and self-driving cars along with IoT is expanding beyond the scope of existing cybersecurity areas such as information corruption, leakage, and service interference [7,8]. It is even expanding into areas where safety of human life and property must be guaranteed.…”

Section: Introductionmentioning

confidence: 99%

Efficient Autonomous Defense System Using Machine Learning on Edge Device

Cho¹

2022

Computers, Materials &Amp; Continua

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As a large amount of data needs to be processed and speed needs to be improved, edge computing with ultra-low latency and ultra-connectivity is emerging as a new paradigm. These changes can lead to new cyber risks, and should therefore be considered for a security threat model. To this end, we constructed an edge system to study security in two directions, hardware and software. First, on the hardware side, we want to autonomically defend against hardware attacks such as side channel attacks by configuring field programmable gate array (FPGA) which is suitable for edge computing and identifying communication status to control the communication method according to priority. In addition, on the software side, data collected on the server performs end-to-end encryption via symmetric encryption keys. Also, we modeled autonomous defense systems on the server by using machine learning which targets to incoming and outgoing logs. Server log utilizes existing intrusion detection datasets that should be used in real-world environments. Server log was used to detect intrusion early by modeling an intrusion prevention system to identify behaviors that violate security policy, and to utilize the existing intrusion detection data set that should be used in a real environment. Through this, we designed an efficient autonomous defense system that can provide a stable system by detecting abnormal signals from the device and converting them to an effective method to control edge computing, and to detect and control abnormal intrusions on the server side.

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“…Using an autoregressive latent variable model, they found that the emotional responses to attack evolved independently over time. Next, Randanliev et al (2021) compared 12 cyber risk assessment techniques applied to IoT systems. Based on their findings, they described a goal-oriented dependency modeling approach for assessing risk states in IoT systems.…”

mentioning

confidence: 99%