Detecting Cyber Supply Chain Attacks on Cyber Physical Systems Using Bayesian Belief Network

Yeboah-Ofori, Abel; Islam, Shareeful; Brimicombe, Allan J.

doi:10.1109/icsiot47925.2019.00014

Cited by 14 publications

(7 citation statements)

References 11 publications

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“…The development of the SG, a highly secure, reliable, and environmentally friendly national power grid system, is driven by concerns about greenhouse gas emissions like carbon dioxide (CO2) and the need for more reliable and efficient power transmission and distribution ( Yeboah-Ofori, Islam & Brimicombe, 2019 ). In conclusion, an SG transmits data in both directions to transfer electricity from generators to end users.…”

Section: Related Workmentioning

confidence: 99%

“…A large number of distributed renewable energy sources, live, real-time data communication about tariff increases and energy use between consumers and service providers, infrastructure to collect and transfer statistics about grid parameters for analysis, and the capability to act in response to these analyses are all necessary components of a smart grid ( Parimi & Chakraborty, 2020 ). The smart energy grid generates much data and information that must be transmitted, processed, and stored for efficient decision-making ( Yeboah-Ofori, Islam & Brimicombe, 2019 ). Because it has so many uses, the Internet of Things might be a perfect fit for the smart energy system.…”

Section: Introductionmentioning

confidence: 99%

“…We have optimism for a solution because of the potential of IoT to improve power quality and dependability, two of the biggest problems with the power system. By including intelligent information-processing capabilities during the flow of electricity from the service provider to the customers, energy monitoring infrastructure and innovative metering technologies can help transform a traditional power grid system into an intelligent grid system ( Yeboah-Ofori, Islam & Brimicombe, 2019 ). This integrated system may gather information on energy use, voltage levels, current flows, and phase angles, among other things.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Security risk models against attacks in smart grid using big data and artificial intelligence

Yasin Ghadi,

Mazhar,

Aurangzeb

et al. 2024

PeerJ Computer Science

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The need to update the electrical infrastructure led directly to the idea of smart grids (SG). Modern security technologies are almost perfect for detecting and preventing numerous attacks on the smart grid. They are unable to meet the challenging cyber security standards, nevertheless. We need many methods and techniques to effectively defend against cyber threats. Therefore, a more flexible approach is required to assess data sets and identify hidden risks. This is possible for vast amounts of data due to recent developments in artificial intelligence, machine learning, and deep learning. Due to adaptable base behavior models, machine learning can recognize new and unexpected attacks. Security will be significantly improved by combining new and previously released data sets with machine learning and predictive analytics. Artificial Intelligence (AI) and big data are used to learn more about the current situation and potential solutions for cybersecurity issues with smart grids. This article focuses on different types of attacks on the smart grid. Furthermore, it also focuses on the different challenges of AI in the smart grid. It also focuses on using big data in smart grids and other applications like healthcare. Finally, a solution to smart grid security issues using artificial intelligence and big data methods is discussed. In the end, some possible future directions are also discussed in this article. Researchers and graduate students are the audience of our article.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Security risk models against attacks in smart grid using big data and artificial intelligence

Yasin Ghadi,

Mazhar,

Aurangzeb

et al. 2024

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…Despite the fact that the framework that was proposed was successful, there is still opportunity for growth and development within it. The authors of [36] constructed a cyberattack within the AI industry using BBN as a framework in order to gain a better understanding of the vulnerabilities of CSC as well as the consequences of uncertainty. In the paper [37], the authors presented a method that makes use of deep learning to design an effective and promising NIDS by combining ICs and ICS.…”

Section: Related Workmentioning

confidence: 99%

Using machine learning algorithm for detection of cyber-attacks in cyber physical systems

Almajed¹,

Ibrahim²,

Abualkishik³

et al. 2022

PEN

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Network integration is common in cyber-physical systems (CPS) to allow for remote access, surveillance, and analysis. They have been exposed to cyberattacks because of their integration with an insecure network. In the event of a violation in internet security, an attacker was able to interfere with the system's functions, which might result in catastrophic consequences. As a result, detecting breaches into mission-critical CPS is a top priority. Detecting assaults on CPSs, which are increasingly being targeted by cyber criminals and cyber threats, is becoming increasingly difficult. It is potential that (AI) Artificial Intelligence as well as (ML) Machine Learning will make this the worst of times, but it also has the potential to be the best of times. There are a variety of ways in which AI technology can aid in the growth and profitability of a variety of industries. Such data can be parsed using ML and AI approaches in designed to check attacks on CPSs. We present the new framework for the detection of cyberattacks, which makes use of AI and ML. We begin a process to cleaning up the data in the CPS database by applying normalization to eliminate errors and duplication. The features are obtained by using a technique known as Linear Discriminant Analysis (LDA). We have suggested the SFL-HMM together with HMS-ACO process as a method used for detection of the cyberattacks. A MATLAB simulation used to evaluate the new strategy, and the metrics obtained from that simulation are compared to those obtained from the older methods. According to the findings of several studies, the framework is significantly more effective than conventional methods in maintaining high levels of privacy. In addition, the framework outperforms conventional detection algorithms in words of detection rate, the rate of the false positive, and calculation time, respectively.

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“…Prior research activities have focused on securing the supply chain during pre-acquisition and acquisition activities [3][4][5][6][7][8][9][10][11][12][13]. In this work, we propose a novel application of ultra-wideband (UWB) radar technology to provide the capability to start addressing the aforementioned supply chain threats at the asset owner level (end of the supply chain) where the threat materializes and has a measurable impact.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Feasibility of Utilizing a Low-Cost UWB Radar for Hardware Implant & Counterfeit Device Detection

Cooke¹,

Reed²,

Mullins³

et al. 2023

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Hardware implants & counterfeit devices in the US power grid pose a significant national security threat. Asset owners currently have few options for detecting the presence of such devices "in the wild". The goal of this work is to develop a non-invasive sensing method to solve this problem. In this work we evaluate the feasibility of implementing a nonlinear UWB radar tomography system to differentiate between electronic internals of externally-similar devices using low-cost off-the-shelf hardware.

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Detecting Cyber Supply Chain Attacks on Cyber Physical Systems Using Bayesian Belief Network

Cited by 14 publications

References 11 publications

Security risk models against attacks in smart grid using big data and artificial intelligence

Security risk models against attacks in smart grid using big data and artificial intelligence

Using machine learning algorithm for detection of cyber-attacks in cyber physical systems

Evaluation of the Feasibility of Utilizing a Low-Cost UWB Radar for Hardware Implant & Counterfeit Device Detection

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