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2024 Asia Communications and Photonics Conference, ACP 2024 and International Conference on information Photonics and Optical Communications, IPOC 2024

作者： Sheng, Yajie Chen, Bin Lei, Yi Deng, Jingxin Xu, Jiwei Fu, Mengfan Zhuge, Qunbi Li, Shen Department of Computer Science and Information Engineering Hefei University of Technology Hefei China State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China Department of Electrical and Computer Engineering Université Laval Québec Canada

Performance of concatenated multilevel coding with probabilistic shaping (PS) and Voronoi constellations (VCs) is analysed over AWGN channel. Numerical results show that VCs provide up to 1.3 dB SNR gains over PS-QAM ... 详细信息

ISBN: (纸本)9798350379266

关键词： Probability distributions

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User-Centric Security and Privacy Threats in Connected Vehicles: A Threat Modeling Analysis Using STRIDE and LINDDUN

User-Centric Security and Privacy Threats in Connected Vehic...

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2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and Big Data Computing, 2023 International Conference on Cyber science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Stingelova, Beata Thrakl, Clemens Thaddaus Wronska, Laura Jedrej-Szymankiewicz, Sandra Khan, Sajjad Svetinovic, Davor Vienna University of Economics and Business Information Systems and Operations Management Vienna Austria Khalifa University Center for Cyber-Physical Systems Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350304602

The increasing equipment of cars with smart systems and their networking with other devices is leading to a growing network of connected vehicles. Connected cars are Internet of Things (IoT) devices that communicate bidirectionally with other systems, enabling internet access and data exchange. Artificial Intelligence (AI) offers benefits such as autonomous driving, driver assistance programs, and monitoring. The increasing connectivity of cars also brings new risks to users' privacy. Our study focuses on privacy threats in connected cars from a user perspective. Our study provides a comprehensive threat model analysis based on a combination of STRIDE and LINDDUN. We analyze the various threats and vulnerabilities that arise from connecting cars to the internet and other devices, including Vehicle-to-Vehicle (V2V), Vehicle-to-Vloud (V2C), and Vehicle-to-Device (V2D). We conduct our study based on a theoretical model of a modern-day connected vehicle of another study. Our study shows that several types of threats can negatively impact the privacy of connected car users. This encapsulates the potential risks, such as the inadvertent disclosure of personal data due to the vehicle's interconnectedness with other devices, including smartphones, and the subsequent susceptibility to unauthorized access, while also highlighting the need for robust security measures indicated by our comprehensive threat modeling, to safeguard against a wide array of identified cybersecurity threats. © 2023 IEEE.

关键词： Electronic data interchange

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Week Ahead Electricity Power and Price Forecasting Using Improved DenseNet-121 Method

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computers, Materials & Continua 2022年第9期72卷 4249-4265页

作者： Muhammad Irfan Ali Raza Faisal Althobiani Nasir Ayub Muhammad Idrees Zain Ali Kashif Rizwan Abdullah Saeed Alwadie Saleh Mohammed Ghonaim Hesham Abdushkour Saifur Rahman Omar Alshorman Samar Alqhtani Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Department of Computer Science COMSATS University IslamabadWah Campus22060Pakistan Faculty of Maritime Studies King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Federal Urdu University of ArtsScience and TechnologyIslamabad44000Pakistan School of Electrical Engineering and Computer Science National University of Science and TechnologyIslamabad44000Pakistan Department of Computer Science and Engineering University of Engineering and TechnologyNarowal CampusLahore54000Pakistan Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia

In the Smart Grid(SG)residential environment,consumers change their power consumption routine according to the price and incentives announced by the utility,which causes the prices to deviate from the initial ***,electricity demand and price forecasting play a significant role and can help in terms of reliability and *** to the massive amount of data,big data analytics for forecasting becomes a hot topic in the SG *** this paper,the changing and non-linearity of consumer consumption pattern complex data is taken as *** minimize the computational cost and complexity of the data,the average of the feature engineering approaches includes:Recursive Feature Eliminator(RFE),Extreme Gradient Boosting(XGboost),Random Forest(RF),and are upgraded to extract the most relevant and significant *** this end,we have proposed the DensetNet-121 network and Support Vector Machine(SVM)ensemble with Aquila Optimizer(AO)to ensure adaptability and handle the complexity of data in the ***,the AO method helps to tune the parameters of DensNet(121 layers)and SVM,which achieves less training loss,computational time,minimized overfitting problems and more training/test *** evaluation metrics and statistical analysis validate the proposed model results are better than the benchmark *** proposed method has achieved a minimal value of the Mean Average Percentage Error(MAPE)rate i.e.,8%by DenseNet-AO and 6%by SVM-AO and the maximum accurateness rate of 92%and 95%,respectively.

关键词： Smart grid deep neural networks consumer demand big data analytics load forecasting price forecasting

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Safeguarding Healthcare: A Comprehensive Threat Analysis of Clinical Decision Support systems

Safeguarding Healthcare: A Comprehensive Threat Analysis of ...

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作者： Hamel, Aleksandra Ursula Charlotte Zarcu, Bogdan-Cristian Csenteri, Andras-Gergely Pfliegler, Tamara Khan, Sajjad Svetinovic, Davor Vienna University of Economics and Business Information Systems and Operations Management Vienna Austria Khalifa University Center for Cyber-Physical Systems Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350304602

Using digital data gathering and analytics in healthcare brings benefits and risks to patients and practitioners. Smart Health information systems, such as Clinical Decision Support systems (CDSSs), consolidate data from various sources, utilizing artificial intelligence for decision support. However, machine learning models in CDSSs are vulnerable to various attacks, leading to incorrect predictions with severe consequences. This paper systematically investigates security and privacy threats related to CDSSs. First, we leverage the data flow and sequence diagrams to identify the critical use cases that might lead to security or privacy breaches. Second, we identify and classify threats imminent to the CDSSs using Security Cards and STRIDE. Lastly, the persona non-grata who pose a significant threat to the integrity of the CDSSs are identified. Implementing our method can assist teams in addressing security threats to CDSSs by considering their unique vulnerabilities. This research contributes to developing comprehensive security strategies for CDSSs. © 2023 IEEE.

关键词： Artificial intelligence

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Cyber Vulnerabilities in Blockchain Electronic Health Records: An In-Depth Threat Analysis

Cyber Vulnerabilities in Blockchain Electronic Health Record...

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作者： Brkic, Marija Dinu, Horia-Stefan Mirkovic, Alida Sabirovic, Alma Khan, Sajjad Svetinovic, Davor Vienna University of Economics and Business Information Systems and Operations Management Vienna Austria Center for Cyber-Physical Systems Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350304602

The rapid evolution of information systems and the surge in data generation and dissemination have fostered the emergence of Electronic Health Records (EHRs). Specifically, blockchain-enabled EHRs are revolutionizing modern healthcare by centralizing patient data promoting transparency, accessibility, and interoperability. However, amidst increasing cyberattacks on healthcare institutions, the security of these EHRs is a critical concern. Existing research predominantly focuses on the advantages and disadvantages of incorporating blockchain technology into EHRs, leaving a noticeable gap in analyzing specific threats. Our study bridges this gap by leveraging a hybrid threat modeling methodology, integrating attack tree models with an exhaustive review of existing literature on threats to blockchain-based standardized EHRs. This approach has identified and organized 26 unique threats into an attack tree. Each threat is elaborated upon and discussed within the context of the Blockchain EHRs. © 2023 IEEE.

关键词： Blockchain

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Blockchain CBDC Security Threats Using STRIDE 5

Blockchain CBDC Security Threats Using STRIDE

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5th International Conference on Blockchain Computing and Applications, BCCA 2023

作者： Hans, Jaqueline Khan, Sajjad Svetinovic, Davor Information Systems and Operations Management Vienna University of Economics and Business Vienna Austria Center for Cyber-Physical Systems Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350339239

In strategic response to the increasing threats from crypto payment systems, cryptocurrencies, and stablecoins, central banks globally are contemplating the introduction of their digital currencies, termed Central Bank Digital Currencies (CBDCs). This trend, especially the emergence of CBDCs built on blockchain technology, instigates significant discourse on security and privacy concerns. This research paper delves into the potential security risks of blockchain-based CBDCs, distinguishing between permissionless and permissioned network architectures and token-based and account-based access mechanisms. We employed STRIDE, a threat modeling methodology, to elucidate these risks, on architectural constructs derived from CBDC proposals and use cases, identifying 39 distinct threats. Our study intends to enrich the ongoing dialogue on the evolution of blockchain-based CBDCs. The findings provide a robust foundation to guide and inform prudent design decisions by offering a detailed understanding of potential security and privacy risks, thereby contributing to developing more secure CBDCs. © 2023 IEEE.

关键词： Blockchain

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Threat Modeling for ML-based Topology Prediction in Vehicular Edge Computing Architecture

Threat Modeling for ML-based Topology Prediction in Vehicula...

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作者： Doan, Hong Hanh Paul, Audri Adhyas Zeindlinger, Harald Zhang, Yiheng Khan, Sajjad Svetinovic, Davor Vienna University of Economics and Business Information Systems and Operations Management Vienna Austria Center for Cyber-Physical Systems Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350304602

The Internet of Vehicles (IoV), a network that interlinks vehicles, infrastructure, and assorted entities, serves as a cornerstone for intelligent transportation systems and the emergence of smart cities. Within this context, edge computing has been identified as a critical solution for providing rapid and reliable data processing. Machine Learning (ML) techniques have become essential to IoV activities such as resource allocation and load balancing across mobile edge servers, typified by decentralized services that range from natural language processing to image recognition. The fusion of ML with edge computing within IoV architecture promises enhanced performance, efficiency, and safety. However, this amalgamation also creates challenges related to data privacy, cybersecurity, malfunctioning edge devices, inconsistent network connectivity, human errors, and malicious insiders. Consequently, this paper focuses on modeling security threats within an ML-based edge computing framework for the IoV. We analyze the system provided by the Linux Foundation Edge Akraino Project's Stable Topology Prediction blueprint by employing a hybrid threat modeling technique. Our strategy leverages STRIDE to elicit threats on distinct system elements like vehicle-to-vehicle communication networks, edge networks, and ML models. Subsequently, these threats are consolidated for a comprehensive view using an attack tree. © 2023 IEEE.

关键词： Machine learning

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Improving Graph Machine Learning Performance Through Feature Augmentation Based on Network Control Theory 32

Improving Graph Machine Learning Performance Through Feature...

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32nd Mediterranean Conference on Control and Automation, MED 2024

作者： Said, Anwar Ahmad, Obaid Ullah Abbas, Waseem Shabbir, Mudassir Koutsoukos, Xenofon Vanderbilt University Computer Science Department NashvilleTN United States University of Texas at Dallas Electrical Engineering Department RichardsonTX75080 United States University of Texas at Dallas Systems Engineering Department RichardsonTX United States Information Technology University Computer Science Department Lahore Pakistan

ISBN: (纸本)9798350395440

Network control theory (NCT) offers a robust analytical framework for understanding the influence of network topology on dynamic behaviors, enabling researchers to decipher how certain patterns of external control measures can steer system dynamics towards desired states. Distinguished from other structure-function methodologies, NCT's predictive capabilities can be coupled with deploying Graph Neural Networks (GNNs), which have demonstrated exceptional utility in various network-based learning tasks. However, the performance of G NN s heavily relies on the expressiveness of node features, and the lack of node features can greatly degrade their performance. Furthermore, many real-world systems may lack node-level information, posing a challenge for GNNs. To tackle this challenge, we introduce a novel approach, NCT-based Enhanced Feature Augmentation (NCT-EFA), that assimilates average controllability, along with other centrality indices, into the feature augmentation pipeline to enhance GNNs performance. Our evaluation of NCT-EFA, on six benchmark GNN models across two experimental setting-solely employing average controllability and in combination with additional centrality metrics-showcases an improved performance reaching as high as 11 %. Our results demonstrate that incorporating NCT into feature enrichment can substantively extend the applicability and heighten the performance of GNNs in scenarios where node-level information is unavailable. © 2024 IEEE.

关键词： Graph neural networks

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Distributed Optimization with Finite Bit Adaptive Quantization for Efficient Communication and Precision Enhancement 63

Distributed Optimization with Finite Bit Adaptive Quantizati...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Rikos, Apostolos I. Jiang, Wei Charalambous, Themistoklis Johansson, Karl H. Artificial Intelligence Thrust of the Information Hub Guangzhou China The Hong Kong University of Science and Technology Department of Computer Science and Engineering Clear Water Bay Hong Kong Aalto University School of Electrical Engineering Department of Electrical Engineering and Automation Espoo Finland University of Cyprus School of Engineering Department of Electrical and Computer Engineering Nicosia1678 Cyprus Kth Royal Institute of Technology Division of Decision and Control Systems StockholmSE-100 44 Sweden

ISBN: (纸本)9798350316339

In realistic distributed optimization scenarios, individual nodes possess only partial information and communicate over bandwidth constrained channels. For this reason, the development of efficient distributed algorithms is essential. In our paper we addresses the challenge of unconstrained distributed optimization. In our scenario each node's local function exhibits strong convexity with Lipschitz continuous gradients. The exchange of information between nodes occurs through 3-bit bandwidth-limited channels (i.e., nodes exchange messages represented by a only 3 -bits). Our proposed algorithm respects the network's bandwidth constraints by leveraging zoom-in and zoom-out operations to adjust quantizer parameters dynamically. We show that during our algorithm's operation nodes are able to converge to the exact optimal solution. Furthermore, we show that our algorithm achieves a linear convergence rate to the optimal solution. We conclude the paper with simulations that highlight our algorithm's unique characteristics. © 2024 IEEE.

关键词： Bandwidth

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Innovative Data Encryption Techniques using AI for Wireless Sensor Actuator Network Security 2

Innovative Data Encryption Techniques using AI for Wireless ...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Dharmateja, M. Rama, Prabha K.P. Asha, N. Nithya, P. Lalitha, S. Manojkumar, P. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Arunai Engineering College Department of Computer Science and Engineering Tamil Nadu Tiruvannamalai India Bannari Amman Institute of Technology Department of Electrical and Electronics Engineering Tamil Nadu Sathyamangalam India

ISBN: (纸本)9798350382952

Wireless Sensor Actuator Networks (WSANs) are sensitive to cyber threats due to present static encryption techniques. Adaptive security methods are required in WSAN settings to reduce evolving dangers and maintain data integrity. The paper introduces a novel method for encrypting WSAN data using AI and machine learning. Long Short-Term Memory (LSTM) networks process data, Genetic Algorithms (GAs) optimize encryption, and Isolation Forests detect threats in real-time. The proposed system achieved remarkable performance, with scores of 98% data integrity, 96% error repair, and 95% threat detection accuracy. Encryption rates reach their max at 200 Mbps, with optimal resource usage (30% CPU, 25% RAM) and a 5% false positive rate. The proposed AI-driven security architecture improves data integrity, encryption speed, and overall security efficacy in WSANs, surpassing existing methods. The paper overcomes the limitations of static encryption methods and proposes a proactive protection mechanism against developing cyber threats, making a substantial contribution to WSAN security improvement. © 2024 IEEE.

关键词： Cryptography

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