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security, Trust, and Privacy in cyber-physical Systems (CPS)

Security, Trust, and Privacy in Cyber-physical Systems (CPS)

引用

cyber Physical Systems, Power Electronics and Electric Vehicles (ICPEEV), International Conference on

作者： Wareez A. Busari Abeeb A. Bello Department of Cyber Security Science Federal University of Technology Minna Niger State Nigeria Department of Electrical and Computer Engineering Mahindra University Hyderabad India

ISBN: (数字)9798350388664

ISBN: (纸本)9798350388671

This paper explores the convergence of physical and computational components in cyber-Physical Systems (CPS). security, trust, and privacy are paramount for reliable and resilient operation of these interconnected systems across diverse sectors like healthcare and smart cities. The paper analyses the complex interplay of these factors, identifying practical challenges and proposing solutions to enhance system robustness. By analysing trends, research gaps, and offering actionable recommendations, the study aims to empower stakeholders in fortifying CPS against evolving cyber threats. It emphasizes the need for comprehensive strategies to mitigate risks, protect sensitive information, and ensure data integrity and system reliability. Through a holistic approach integrating security, trust, and privacy mechanisms, the research contributes to advancing effective security measures in CPS, ultimately fostering a culture of trust and resilience in the digital era.

关键词： Privacy Smart cities Data integrity Interconnected systems Medical services cyber-physical systems Robustness security Stakeholders Resilience

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Watermarking and Encryption for Electronic Medical Records (EMR) security Based on mCloud 2

Watermarking and Encryption for Electronic Medical Records (...

引用

2nd International Conference on cyber Resilience, ICCR 2024

作者： Abdulshaheed, Haider Rasheed Abbas, Yaseen Saadi Tawfeq, Jamal F. Ateeq, Karamath Alnawayseh, Saif. E. A. Ghazal, Taher M. Baghdad College Of Economic Sciences University Computer Engineering Techniques Department Baghdad Iraq College Of Engineering Al-Nahrain University Electronic And Communications Engineering Department Baghdad Iraq Medical Technical College Al-Farahidi University Department Of Medical Instrumentation Technical Engineering Baghdad10070 Iraq School Of Computing Skyline University College University City Sharjah Sharjah United Arab Emirates College Of Engineering Mutah University Department Of Electrical Engineering Al-Karak Jordan Khalifa University Centre For Cyber Physical Systems Computer Science Department United Arab Emirates Center For Cyber Security Faculty Of Information Science And Technology Selangor Bangi43600 Malaysia

ISBN: (纸本)9798350394962

Providing quality healthcare services and promoting collaborative clinical research are easier and more efficient with electronic medical record (EMR) systems. All aspects of care are included in electronic health records. Data characterized by volume, variety, and velocity is what IBM defines as big data. Therefore, we evidently have a significant problem managing 'big EMR data' from a volume, accuracy, and velocity perspective. Data misuse and patient safety are significant risks associated with poorly implemented EMR-based systems. Storage, access, and retrieval of big data are all aspects of security to consider. Location is another important aspect of secure data use, in addition to strong access control mechanisms. Researchers are investigating secure mobile data usage based on location after recent reports of illegal trade and theft of patient data over mobile devices. To improve work flow dynamics and efficiency, healthcare facilities are increasingly using mobile devices. In spite of this, mobile devices are becoming more and more common. A new technique for preventing EMR infiltration and manipulation by hackers using the cloud is presented in this article. © 2024 IEEE.

关键词： Authentication

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Traffic Congestion Prediction Using Machine Learning: Amman City Case Study

Traffic Congestion Prediction Using Machine Learning: Amman ...

引用

2024 International Conference on Medical Imaging, Electronic Imaging, Information Technologies, and Sensors, MIEITS 2024

作者： Arabiat, Areen Hassan, Mohammad Al Momani, Omar Department of Communication and Computer Engineering Faculty of Engineering Al Ahliyya Amman University Amman19111 Jordan Department of Networks security Faculty of Information Technology The World Islamic Sciences and Educations University WISE Amman Jordan

ISBN: (纸本)9781510680814

Traffic congestion leads to wasted time, pollution, and increased fuel usage. Traffic congestion prediction has become a developing research topic in recent years, particularly in the field of machine learning (ML). The evaluation of various traffic parameters is used to predict traffic congestion by relying on historical data. In this study, we will predict traffic congestion in Amman city specifically at Northbound Street at 8th Circle using different ML classifiers. The 8th Circle links four main streets: Westbound, Northbound, Eastbound, and Southbound. Because of the heavy traffic on Northbound, it was chosen to predict traffic congestion. Datasets were collected from the greater Amman municipality hourly based. Datasets were divided and per-process to be understandable. The Logistic Regression, Decision Tree (DT), Random Forest, and multilayer perceptron (MLP) classifiers have been chosen to predict traffic congestion at Northbound Street linked with the 8th Circle. Four experiments were run under The Waikato Environment for Knowledge Analysis (WEKA) tools to find the best classifiers to predict traffic congestion at Northbound. The chosen classifiers have been evaluated using F-Measure, sensitivity, precision, and accuracy evaluation metrics. The obtained results from all experiments have demonstrated the Logistic Regression is the best classifier to predict traffic congestion. The accuracy of Logistic Regression to predict traffic congestion at Northbound Street was97.6%. © 2024 SPIE.

关键词： Traffic congestion

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Leveraging IR based sequence and graph features for source-binary code alignment 4

Leveraging IR based sequence and graph features for source-b...

引用

4th International Conference on Neural Networks, Information and Communication engineering, NNICE 2024

作者： Yu, Zhouqian Zhang, Wei Xu, Tao Beijing Information Science and Technology University Computer School Beijing China Beijing Information Science and Technology University Beijing Laboratory of National Economic Security Early-Warning Engineering Beijing China Tsinghua University Department of Computer Science and Technology Beijing China

ISBN: (纸本)9798350394375

Code similarity analysis is a versatile technique that can be applied across various domains, including code clone detection, code search, malware detection, patch analysis, and vulnerability search. The core of source-binary code similarity analysis lies in effective modeling of the both of source code and binary code. Existing researches have overlooked the fact that code has both text and graph structure features, so they usually only use one of these features for modeling. In this paper, we propose a method that combines sequence features based on intermediate representation and program graph features derived from intermediate representation to enhance the capture of program semantics. Our approach involves the parallel embedding and learning of LLVM IR and the derived program graphs, leading to the extraction of the final code feature representation. Furthermore, we employ a triplet-loss network to identify code characteristic disparities and produce similarity rankings. The experimental results have demonstrated that our approach outperforms other models on the baseline, achieving a 6% improvement in accuracy for source-binary code similarity tasks. © 2024 IEEE.

关键词： Graph neural networks

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AI-Driven Intelligent IoT Systems for Real-Time Food Quality Monitoring and Analysis 2

AI-Driven Intelligent IoT Systems for Real-Time Food Quality...

引用

2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Krishnan, Prabhakar Ebenezar, U. Samson Ranitha, R. Purushotham, N. Balakrishnan, T. Suresh Amrita Vishwa Vidyapeetham Center for Cyber Security Systems and Networks Amritapuri campus India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India Mohan Babu University Department of Computer Science and Engineering Mr.palliAP India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350384277

The system will monitor and analyse food quality along the supply chain in real time. The system has high performance due to the CNN algorithms. In terms of accuracy, CNN has a high performance (95.2%), precision (92.8%), recall (94.5%) and F1 score (93.6 %). These features enable the model to predict food quality under good and poor conditions, thus demonstrating its effectiveness in assessing quality. The accuracy of real-time monitoring was evaluated across the supply chain to prove system reliability. Accuracy ratings for transport, storage and processing were 96.3%, 94.7% and 92.1%. These results show the robustness of this system within dynamic supply chain environments. Further testing evaluated environmental resilience capabilities. Under normal conditions, the accuracy of this system was 95.2%, while it reduced to 92.6% and rises again up to a level of 94.3%. The adaptability of the system under harsh conditions demonstrates that it can be applied to ensure food quality in various cases. This study reveals how proposed AI-based intelligent Internet of Things system may be used for real application using an appropriate dataset selected properly. This approach can help improve the quality assurance of food in an industry that is complex by uniting advanced algorithms with real-time data monitoring. © 2024 IEEE.

关键词： Artificial Intelligence Food Quality Analysis Intelligent IoT Systems Real-Time Monitoring

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Intelligent Model to Predict Early Liver Disease using Machine Learning Technique

Intelligent Model to Predict Early Liver Disease using Machi...

引用

2022 International Conference on Business Analytics for technology and security, ICBATS 2022

作者： Ghazal, Taher M. Rehman, Aziz Ur Saleem, Muhammad Ahmad, Munir Ahmad, Shabir Mehmood, Faisal Faculty of Information Science and Technology Ukm Network and Communication Technology Lab Center for Cyber Security Malaysia School of Computer Science NCBAandE Lahore Pakistan Gachon University Department of Computer Engineering Korea Republic of Gachon University Department of It Convergence Engineering Korea Republic of

ISBN: (纸本)9781665409209

Liver Disease (LD) is the main cause of death worldwide, affecting a large number of people. A variety of factors affect the liver, resulting in this disease. The diagnosis of this condition is both expensive and time-consuming. Machine Learning offers a lot of potential in terms of automated disease diagnosis. As a result, the purpose of this research is to assess the efficacy of various Machine Learning (ML) algorithms to lower the high cost of liver disease diagnosis through prediction. With the current rise in numerous liver disorders, it's more important than ever to detect liver disease early on. This research proposed intelligent model to predict liver disease using machine learning technique. This proposed model is more effective and comprehensive in terms of performance of 0.884 accuracy, and 0.116 miss-rate. © 2022 IEEE.

关键词： Forecasting

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Conditional GANs against DNS based unsupervised detection of malicious domains

Conditional GANs against DNS based unsupervised detection of...

引用

2022 International Conference on cyberworlds, CW 2022

作者： Mohus, Mathias Lundteigen Flakk, Emil Henry Norwegian University of Science and Technology Department of Computer Science Trondheim Norway Cyber Security Center for Research and Education Sikt Trondheim Norway

ISBN: (数字)9781665468145

ISBN: (纸本)9781665468145

While Unsupervised Learning methods have been employed in detecting malicious domains using DNS data, it is unknown if these techniques are robust in an adversarial setting. We present a novel method using a conditional GAN, where DNS data could be generated to evade detection, while maintaining adversarial functionality. This could also be employed defensively, by using the discriminator as a detector. © 2022 IEEE.

关键词： Unsupervised learning

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Detection of Network Attacks using Machine Learning and Deep Learning Models

Detection of Network Attacks using Machine Learning and Deep...

引用

2022 International Conference on Machine Learning and Data engineering, ICMLDE 2022

作者： Dhanya, K.A. Vajipayajula, Sulakshan Srinivasan, Kartik Tibrewal, Anjali Kumar, T. Senthil Kumar, T. Gireesh TIFAC-CORE in Cyber Security Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India STSM Architect IBM Security IBM India Sr. Architect IBM Security IBM India Pvt Ltd India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

Anomaly-based network intrusion detection systems are highly significant in detecting network attacks. Robust machine learning and deep learning models for identifying network intrusion and attack types are proposed in this paper. Proposed models have experimented with the UNSW-NB15 dataset of 49 features for nine different attack samples. Decision Tree classifier produced the best accuracy of 99.05% compared to ensemble models - Random forest(98.96%), Adaboost(97.87%), and XGBoost(98.08%). K-Nearest Neighbour classifier trained for various values of K and best performance obtained for K=7 with the accuracy of 95.58%. A Deep Learning model with two dense layers with ReLU activation and a third dense layer with a Sigmoid activation function is designed for binary classification and produced good accuracy of 98.44% with ADAM optimizer, 80:20 Train-Test Split Ratio. Network attack exploits are detected with an accuracy 95% by XGBoost, Fuzzers attack with 90% accuracy by Random Forest, Generic attacks with 99% accuracy by Random Forest, and Reconnaissance attacks with 79% by Decision Trees. All features are relevant and strong in network attack detection, which eliminates the requirement of feature selection. © 2022 Elsevier B.V.. All rights reserved.

关键词： Network security

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Optimizing Cloud-Based IoT Architectures with an Energy-Sensitive Load Balancing Algorithm for Enhanced Resource Utilization and Network Efficiency 4

Optimizing Cloud-Based IoT Architectures with an Energy-Sens...

引用

4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Kumar, Ravi Raghavendar, K. Chary, Kalvog Prakasha Naresh, K. Reddy, G. Venkata Rami Kumar, D. Sravan Sreenidhi University Department of Cse Hyderabad India Teegala Krishna Reddy Engineering College Department of Cse Hyderabad India Cvr College of Engineering Department of CSE Cyber Security Hyderabad India Tkr College of Engineering & Technology Department of Cse Hyderabad India Vardhaman College of Engineering Department of Cse Hyderabad India Narshima Reddy Engineering College Maisammaguda Hyderabad India

ISBN: (纸本)9798350387490

Cloud computing, which provides virtualized computing resources online, has become increasingly popular. Effective load balancing (LB) is essential for distributing dynamic workloads among cloud systems and ensuring equitable resource sharing, which prevents server overloads. This study presents a customized cloud-based load balancer algorithm designed for IoT network protocols, evaluating the demands of virtual and physical host machines in cloud environments. The algorithm manages loads within cloud-based IoT architectures to enhance energy efficiency and improve network response times while integrating seamlessly with existing frameworks. Utilizing proportional sharing, the method migrates heavily loaded virtual machines to less strained physical hosts. Key characteristics, including memory specifications, CPU utilization, power consumption, and SLA violation management, are considered. The Updated Energy-Sensitive Balancing Load Algorithm (ESBL) is developed to analyze IoT sensor data and, compared to older approaches, optimizes load balancing and other critical parameters. The proposed load balancer significantly improves resource utilization and network efficiency, ultimately optimizing IoT communication in cloud computing environments. © 2024 IEEE.

关键词： Cloud platforms

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Design of Lightweight Cryptography based Deep Learning Model for Side Channel Attacks 33

Design of Lightweight Cryptography based Deep Learning Model...

引用

33rd International Telecommunication Networks and Applications Conference, ITNAC 2023

作者： Ahmed, Amjed Abbas Hasan, Mohammad Kamrul Nafi, Nazmus Shaker Aman, Azana Hafizah Islam, Shayla Fadhil, Saif Aamer Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia Department of Computer Techniques Engineering Baghdad10011 Iraq Integrated Work Package KBR SouthbankVIC Australia Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur56000 Malaysia

ISBN: (纸本)9798350317138

Depending on a device's encryption mechanism, a wide variety of tangible details could be exposed. These leaks are used in side-channel analysis, which is used to get keys. Due to deep learning's sensitivity to the characteristics of the data being processed, using such algorithms can significantly improve the accuracy and efficiency of side channel analysis. However, classic neural networks are now used for the vast majority of the work that is being done. When the number of nodes in a network grows, so does the efficiency with which key recovery can function. However, the method's computing complexity grows in direct proportion. Overfitting, inadequate capacity for feature extraction, and inefficient training are all potential issues. In this study, we develop a compact convolutional neural network by enhancing a previously existing combination of neural networks. Novel neural network along with previous neural network both have their own implementations of the side-channel analysis used in comparative trials. Statistically, the new network has better accuracy, quicker convergence, and more robustness. As part of the research, heatmaps were provided as a means of data visualisation. The critical interval concentration is higher and the heat value is higher in the new network. Conventional neural networks, which serve as the foundation for various kinds of neural networks, perform much worse than side channel studies based on feature fusion networks. © 2023 IEEE.

关键词： Efficiency

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