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Minimising cybersecurity risk exposures in industrial control system environments: a techno-human vulnerability analysis approach

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Journal of Cyber Security Technology 2024年

作者： Ani, Uchenna Daniel Watson, Jeremy He, Hongmei Radanliev, Petar Epiphaniou, Gregory School of Computer Science and Mathematics Keele University Staffordshire United Kingdom Department of Science Technology Engineering and Public Policy University College London London United Kingdom School of Science Engineering and Environment University of Salford Salford United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom Coventry United Kingdom

Organisations operating IoT-enabled industrial control systems (ICSs) are concerned about growing cybersecurity risks and impacts to their systems. Cyber-attacks on ICSs demonstrate that technology alone is neither a problem nor a solution to the growing cybersecurity issues affecting these systems. As socio-technical systems, ICSs encompass the functions and interactions of social and technological system elements to enable and/or sustain industrial processes. Thus, a more effective cybersecurity risk management process needs to consider human and technology factors, especially for high-value industrial process targets. Combining critical reviews and gap analysis of existing vulnerability assessment methods with conceptual modelling, a Vulnerability Analysis Critical Impact Point Process (VACIP) methodology is proposed which considers both human and technological vulnerabilities within a cyber-physical system environment to inform an improved insight about attack criticality and impacts. VACIP is validated using a simulated industrial mini testbed;showing that it can enable practicable support for security vulnerability discovery, impact criticality analysis, weak link identification, and prioritised controls. Its novelty is demonstrated in its combination of technology and human vulnerability evaluations in the minimisation of system security exposures. It provides a useful guide for adopting effective cybersecurity risk assessment and exposure reduction strategies. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Risk analysis

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Deep Learning based Currency Trend Classification Trained on Technical Indicators based Generated Dataset 27

Deep Learning based Currency Trend Classification Trained on...

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27th International Conference on Knowledge Based and Intelligent Information and engineering Sytems, KES 2023

作者： Tigani, Smail Makrane, Amal Saadane, Rachid Chehri, Abdellah AAIR Lab Digital Engineering and Artificial Intelligence Systems High Private School Casablanca20100 Morocco Electrical Engineering Department SIRC-LaGeS Hassania School of Public Labors Casablanca20250 Morocco Department of Mathematics and Computer Science Royal Military College of Canada KingstonON11 K7K 7B4 Canada

This research paper presents a deep learning-based predictive model for classifying currency trends using technical indicators. The model is trained on a dataset generated from three technical indicators: relative strength index (RSI), moving average convergence divergence (MACD), and stochastic. The dataset consists of historical currency data along with the corresponding values of the technical indicators. The deep learning model can accurately classify the trends of a given currency based on the importance of these indicators. The model's performance is evaluated using standard metrics, and the results demonstrate its effectiveness in classifying currency trends. The proposed model provides a valuable tool for traders and investors in the foreign exchange market by helping them make informed decisions about the direction of currency prices. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Algorithmic Trading Deep Learning Predictive Analytics

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From Lab to Field: Real-World Evaluation of an AI-Driven Smart Video Solution to Enhance Community Safety

arXiv

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arXiv 2023年

作者： Yao, Shanle Ardabili, Babak Rahimi Pazho, Armin Danesh Noghre, Ghazal Alinezhad Neff, Christopher Bourque, Lauren Tabkhi, Hamed Department of Electrical and Computer Engineering University of North Carolina at Charlotte United States Public Policy Program University of North Carolina at Charlotte 9201 University City Blvd CharlotteNC28223 United States

This article adopts and evaluates an AI-enabled Smart Video Solution (SVS) designed to enhance safety in the real world. The system integrates with existing infrastructure camera networks, leveraging recent advancements in AI for easy adoption. Prioritizing privacy and ethical standards, pose based data is used for downstream AI tasks such as anomaly detection. Cloud-based infrastructure and mobile app are deployed, enabling real-time alerts within communities. The SVS employs innovative data representation and visualization techniques, such as the Occupancy Indicator, Statistical Anomaly Detection, Bird’s Eye View, and Heatmaps, to understand pedestrian behaviors and enhance public safety. Evaluation of the SVS demonstrates its capacity to convert complex computer vision outputs into actionable insights for stakeholders, community partners, law enforcement, urban planners, and social scientists. This article presents a comprehensive real-world deployment and evaluation of the SVS, implemented in a community college environment across 16 cameras. The system integrates AI-driven visual processing, supported by statistical analysis, database management, cloud communication, and user notifications. Additionally, the article evaluates the end-to-end latency from the moment an AI algorithm detects anomalous behavior in real-time at the camera level to the time stakeholders receive a notification. The results demonstrate the system’s robustness, effectively managing 16 CCTV cameras with a consistent throughput of 16.5 frames per second (FPS) over a 21-hour period and an average end-to-end latency of 26.76 seconds between anomaly detection and alert issuance. Copyright © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

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Examining the development of attitude scales using Large Language Models (LLMs)

arXiv

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arXiv 2024年

作者： Symeonaki, Maria Stamou, Giorgos Kazani, Aggeliki Tsouparopoulou, Eva Stamatopoulou, Glykeria School of Political Sciences Department of Social Policy Panteion University of Social and Political Sciences Greece School of Electrical and Computer Engineering National Technical University of Athens Greece

For nearly a century, social researchers and psychologists have debated the efficacy of psychometric scales for attitude measurement, focusing on Thurstone’s equal appearing interval scales and Likert’s summated rating scales. Thurstone scales fell out of favour due to the labour-intensive process of gathering judges’ opinions on the initial items. However, advancements in technology have mitigated these challenges, nullifying the simplicity advantage of Likert scales, which have their own methodological issues. This study explores a methodological experiment to develop a Thurstone scale for assessing attitudes towards individuals living with AIDS. An electronic questionnaire was distributed to a group of judges, including undergraduate, postgraduate, and PhD students from disciplines such as social policy, law, medicine, and computer engineering, alongside established social researchers, and their responses were statistically analysed. The primary innovation of this study is the incorporation of an Artificial Intelligence (AI) Large Language Model (LLM) to evaluate the initial 63 items, comparing its assessments with those of the human judges. Interestingly, the AI provided also detailed explanations for its categorisation. Results showed no significant difference between AI and human judges for 35 items, minor differences for 23 items, and major differences for 5 items. This experiment demonstrates the potential of integrating AI with traditional psychometric methods to enhance the development of attitude measurement scales. © 2024, CC BY-NC-SA.

关键词： Students

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The Nexus Between Work and Stress: Challenges with IoE Based Hands-On engineering Education During Lockdown Due to Pandemic

The Nexus Between Work and Stress: Challenges with IoE Based...

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International Conference on Advanced Learning Technologies (ICALT)

作者： Rathin Biswas Vivek Sabanwar Kavi Arya School of Public Policy Indian Insititute of Technology Delhi Delhi India Computer Science & Engineering Indian Institute of Technology Bombay Mumbai India

It has become increasingly important to understand the challenges in IoE-based engineering education due to stress among the academicians who are forced to work remotely due to the lockdown imposed as part of stringent COVID-19 protocol. In this study, an investigation is carried out to understand the nature of stress and its impact on a cohort chosen from academics spread across India. A semi-structured survey investigates the nexus between work & stress among young academicians- how it varies as per demographic, neighborhood, entertainment, lectures, and performance. The study shows that the post-graduate participants and those aged 26–30 years are 14-15% more stressed. Those who are working-from-home in rural areas are more stressed. Participants with the highest levels of stress are found to underrate their own performance as well as others' involvement. It is observed that spending more time on entertainment or developing new interests may reduce stress levels. The study also revealed that carefully designed lectures might lower stress levels as most participants realized. The study contributes to the knowledge base for considering stress associated factors in learning activity.

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Ripeness Classification of Fresh Oil Palm Using Deep Learning

Ripeness Classification of Fresh Oil Palm Using Deep Learnin...

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International electrical engineering Congress (iEECON)

作者： Nathaphat Srithai Krittiporn Rittireung Nongnoot Sungthong Kanabadee Srisomboon Wilaiporn Lee Vera Sa-Ing Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangkok Thailand Broadcasting and Telecommunications Research and Development Fund for Public Interest Office of The National Broadcasting and Telecommunications Commission Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

The challenge of accurately ripeness level classifying from fresh oil palm fruit, this research investigated the problems and methods that were used in ripeness level classification. The goal of this research is to present a method for classifying the ripeness levels of oil palm fruits using deep learning techniques applied to image data. This research applies a deep learning model for classifying the ripeness level of fresh oil palm fruit and aiming for real-time application or mobile usage. The proposed model, Yolov9n (nano), adapts principles from convolutional neural networks that was tuned the learning parameters consist of the Adam optimizer, a learning rate of 0.001, and a batch size of 32, result in the most accurate ripeness level classification model. The research demonstrates the Yolov9n (nano) improved model with optimized parameters to achieve the highest accuracy. Training and validation datasets represent precision of 99.4%, recall of 99.6%, mean average precision at 50% (mAP50) of 99.5%, and mAP50-95 of 92.6%. These findings confirm the model’s effectiveness in accurately classifying the ripeness level of oil palm fruit from images. The study provides guidance for the practical deep learning implementation of ripeness level classification on fresh oil palm imaging for efficient harvesting by farmers cultivating oil palm.

关键词： Deep learning Training YOLO Measurement Adaptation models Accuracy Oils Imaging Real-time systems Convolutional neural networks

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Non-Consensus Opinion Models with Malicious Nodes

SSRN

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SSRN 2023年

作者： Liu, Xinhan Achterberg, Massimo A. Kooij, Robert E. Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Unit ICT Strategy & Policy TNO Netherlands

Opinion dynamics models study how the interaction among people influences the opinion formation process. In most opinion dynamics models, only one opinion can exist in the steady state, which is different from the real-life opinion formation process. In 2009, Shao et al. [1] introduced the Non-Consensus Opinion (NCO) model, which allows different opinions to coexist in the steady state. We extend the NCO model by introducing three type of malicious nodes, namely Byzantine, rebel and random opinion nodes. Byzantine nodes communicate the opposite of their true opinion, rebel nodes follow the minority opinion instead of the majority opinion and random nodes just attain a random opinion at each time step. Several properties of the models are discussed, including the probability of cyclic behavior and the final (average) opinion distribution. We find that the introduction of malicious nodes generally steers towards a more balanced steady state. We additionally demonstrate that for some specific cases that the dynamics of the Byzantine NCO model and the Rebel NCO model are equivalent.[1] J. Shao, S. Havlin, and H. E. Stanley, "Dynamic Opinion Model and Invasion Percolation," Physical Review Letters, vol. 103, no. 1, p. 018701, 2009. © 2023, The Authors. All rights reserved.

关键词： Complex networks

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Keep it Local: Comparing Domain-Specific LLMs in Native and Machine Translated Text using Parallel Corpora on Political Conflict 2

Keep it Local: Comparing Domain-Specific LLMs in Native and ...

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2nd International Conference on Foundation and Large Language Models, FLLM 2024

作者： Osorio, Javier Alsarra, Sultan Converse, Amber Alshammari, Afraa Heintze, Dagmar Khan, Latifur Alatrush, Naif Brandt, Patrick T. D'orazio, Vito Zawad, Niamat Billah, Mahrusa University of Arizona School of Government and Public Policy Tucson United States King Saud University Department of Software Engineering Riyadh Saudi Arabia University of Arizona Department of Linguistics Tucson United States University of Texas - Dallas Department of Computer Science Dallas United States University of Texas - Dallas School of Economic Political and Policy Sciences Dallas United States West Virginia University Department of Political Science Morgantown United States

ISBN: (纸本)9798350354799

The dynamics of political conflict and cooperation require powerful computerized tools capable of effectively tracking security threats and cooperation around the world. This study compares the performance of domain-specific Large Language Models (LLMs) against generically-trained LLMs in binary and multi-class classification using native text in English, Spanish, and Arabic, and their corresponding machine translations. This endeavor yields four key contributions. 1) We present and make available a novel database of annotations using a multi-lingual parallel corpus from the United Nations. 2) Using various metrics, we assess the quality of different machine translation tools. 3) Our results indicate that the ConfliBERT family of LLMs, a set of domain-specific models tailored for political conflict, outperform generically-trained LLMs in English, Spanish, and Arabic in both binary and multi-class tasks. 4) We also disentangle the heterogeneous effects of machine translation on LLM performance in different languages. Overall, results reveal the comparative advantage of native-language domain-specific LLMs specialized on political conflict to understand the dynamics of violence and cooperation worldwide using native text. Our multi-lingual ConfliBERT LLMs provide critical cyber-infrastructure enabling scholars and government agencies use their local languages and information to foster safer, more stable political environments. © 2024 IEEE.

关键词： Machine translation

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Ripeness Classification of Fresh Oil Palm Using Deep Learning

Ripeness Classification of Fresh Oil Palm Using Deep Learnin...

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13th International electrical engineering Congress, iEECON 2025

作者： Srithai, Nathaphat Rittireung, Krittiporn Sungthong, Nongnoot Srisomboon, Kanabadee Lee, Wilaiporn Sa-Ing, Vera King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok Thailand Broadcasting and Telecommunications Research and Development Fund for Public Interest Office The National Broadcasting and Telecommunications Commission Bangkok Thailand

ISBN: (纸本)9798331543952

The challenge of accurately ripeness level classifying from fresh oil palm fruit, this research investigated the problems and methods that were used in ripeness level classification. The goal of this research is to present a method for classifying the ripeness levels of oil palm fruits using deep learning techniques applied to image data. This research applies a deep learning model for classifying the ripeness level of fresh oil palm fruit and aiming for real-time application or mobile usage. The proposed model, Yolov9n (nano), adapts principles from convolutional neural networks that was tuned the learning parameters consist of the Adam optimizer, a learning rate of 0.001, and a batch size of 32, result in the most accurate ripeness level classification model. The research demonstrates the Yolov9n (nano) improved model with optimized parameters to achieve the highest accuracy. Training and validation datasets represent precision of 99.4%, recall of 99.6%, mean average precision at 50% (mAP50) of 99.5%, and mAP50-95 of 92.6%. These findings confirm the model's effectiveness in accurately classifying the ripeness level of oil palm fruit from images. The study provides guidance for the practical deep learning implementation of ripeness level classification on fresh oil palm imaging for efficient harvesting by farmers cultivating oil palm. © 2025 IEEE.

关键词： Convolutional neural networks

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AI-Assisted Markerless Activity Tracking System for Supporting Aging and Wellness

AI-Assisted Markerless Activity Tracking System for Supporti...

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Microwaves Antennas and Propagation Conference (MAPCON)

作者： Hajar Abedi Ahmad Ansariyan Plinio P Morita Alexander Wong Jennifer Boger George Shaker Electrical and Computer Engineering University of Waterloo Waterloo Canada School of Public Health Sciences University of Waterloo Waterloo Canada System Design Engineering University of Waterloo Waterloo Canada

This paper presents a mm-Wave radar-based system to create a novel method of tracking seniors and monitoring them using ambient wireless signals. Radar sensors, coupled with deep learning models, facilitate the identification of various physical activities without requiring physical contact or the use of wearable devices. The system employs range-Doppler maps derived from a real-life in-home activities dataset for training deep learning networks. The gated recurrent units (GRU) model is chosen for real-time implementation due to its optimal trade-off between speed and accuracy. The system achieves an overall accuracy of 93% for trained subjects, with 86% accuracy for classifying the in-home physical activities of a new subject. Additionally, the system captures the frequency of washroom use, sedentary, duration of sleep, active and out-of-home periods, the level of activity performed by the subject over a period of time, current activity state, and gait parameters.

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