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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Suwirmayanti, Ni Luh Gede Pivin Putra, I. Ketut Gede Darma Sudarma, Made Sukarsa, I. Made Setyaningsih, Emy Department of Computer Systems Faculty of Informatics and Computer Institut Teknologi dan Bisnis STIKOM Bali Bali Indonesia Department of Information Technology Faculty of Engineering Udayana University Jimbaran Hill Bali Indonesia Department of Electrical Engineering Faculty of Engineering Udayana University Jimbaran Hill Bali Indonesia Department of Computer Systems Engineering Faculty of Applied Science Universitas AKPRIND Indonesia Yogyakarta Indonesia

ISBN: (纸本)9798331539603

Banking produces extensive and diverse data, so a clustering process is needed to understand customer behavior patterns and transactions more effectively. This clustering has been widely utilized with the K-Means algorithm due to its simplicity and efficiency. However, this algorithm is limited in determining the optimal cluster center, affecting the accuracy of the clustering results. This study proposes applying two metaheuristic algorithms, Invasive Weed Optimization (IWO) and Particle Swarm Optimization (PSO), as additional solutions combined with K-Means. These two algorithms were chosen because of their ability to find global solutions and convergence speed to handle large and complex banking data. The results show the superiority of IWOKM compared to PSOKM in terms of SSE and DBI. From the SSE value, IWOKM produces a lower value (3029.77), which indicates that this method is better at minimizing clustering errors and producing more accurate clustering than PSOKM (80007.09). From the DBI value, IWOKM also produces a better value (1.6684) than PSOKM (1.6782), which shows that the clusters produced by IWOKM are more compact and more separated. However, in terms of computation time, PSOKM is more efficient, with a faster average computation time. Although IWOKM produces better clustering quality, PSOKM offers advantages in terms of processing speed. This finding confirms that the selection of the IWO algorithm is more appropriate for use on datasets with characteristics such as the German Credit Dataset. © 2024 IEEE.

关键词： Invasive weed optimization

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A Framework for Cyber-Secure Monitoring and Safe Operation of Solar PV Microgrids

A Framework for Cyber-Secure Monitoring and Safe Operation o...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Kim, Jaewon Ilic, Marija Xie, Le Massachusetts Institute of Technology Laboratory for Information & Decision Systems CambridgeMA02139 United States Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science CambridgeMA02139 United States Texas A&m University Department of Electrical & Computer Engineering College StationTX77843 United States

ISBN: (纸本)9798350376067

In this paper, we introduce a systematic design of an end-to-end cyber-secure monitoring and safe operation framework for solar photovoltaic (PV)-rich microgrids, by focusing on the use of on-line attack detector for defending against potential cyber-physical attacks and sensor measurement data processing for operational safety. The proposed approach is based on: 1) identifying component models using their input and output measurements;2) dynamic watermarking (DW) methodology for on-line cyber-attack detection and its timing;and 3) an adaptive event-driven safe shutdown procedure after attack detection. It is shown how these three components interactive steps jointly to ensure safe operation under both normal operational scenarios and compromised operational scenarios with some malicious sensors caused by cyber-attacks on solar PV microgrids. Through this on-line integration, the proposed framework dynamically adapts to varying solar irradiance, load demands, operating conditions, and potential cyber-physical attacks. This forms a cyber-physical system which can pro-actively detect and mitigate potential cyber-physical attacks on solar PV microgrids. We describe its proof-of-concept implementation using real-world industrial solar farm data provided by the CenterPoint Energy. This study contributes to enhancing the cyber-security and safe operation of solar PVrich power systems, offering a scalable solution to other types of emerging renewable-rich energy distribution systems. © 2024 IEEE.

关键词： Cyber attacks

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Epistemic Planning for Heterogeneous Robotic systems

Epistemic Planning for Heterogeneous Robotic Systems

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Lauren Bramblett Nicola Bezzo Departments of Systems and Information Engineering and Electrical and Computer Engineering University of Virginia Charlottesville VA USA

In applications such as search and rescue or disaster relief, heterogeneous multi-robot systems (MRS) can provide significant advantages for complex objectives that require a suite of capabilities. However, within these application spaces, communication is often unreliable, causing inefficiencies or outright failures to arise in most MRS algorithms. Many researchers tackle this problem by requiring all robots to either maintain communication using proximity constraints or assuming that all robots will execute a predetermined plan over long periods of disconnection. The latter method allows for higher levels of efficiency in a MRS, but failures and environmental uncertainties can have cascading effects across the system, especially when a mission objective is complex or time-sensitive. To solve this, we propose an epistemic planning framework that allows robots to reason about the system state, leverage heterogeneous system makeups, and optimize information dissemination to disconnected neighbors. Dynamic epistemic logic formalizes the propagation of belief states, and epistemic task allocation and gossip is accomplished via a mixed integer program using the belief states for utility predictions and planning. The proposed framework is validated using simulations and experiments with heterogeneous vehicles.

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Efficient UAV Routing Strategies for Wireless Sensor Network Data Retrieval

Efficient UAV Routing Strategies for Wireless Sensor Network...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Al-Sharhanee, Kareem Ali Malalah Najim, Ali Hamza Alheeti, Khattab M. Ali Satar, Nurhizam Safie Mohd Hashim, Aisha Hassan Abdallah Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq College of Engineering Techniques Department of Communication Technical Engineering Al-Farahidi University Baghdad10011 Iraq Department of Computer Technical Al-Diwaniyah Iraq College of Computer and Information Technology University of Anbar Computer Networking Systems Department Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

WSNs are applied in many disciplines where certain conditions require the ability to adapt to network sink mobility and changes in the dynamics of the area coverage. To meet these needs, it is necessary to develop integrated systems where the ''terrestrial' systems and the Unmanned Aerial Vehicles (UAVs)' collect data from sensors efficiently. Planning of UAV trajectories is another challenging issue if many WSN nodes are randomly deployed. This challenge has a big impact on the overall effectiveness of the system. This paper presents a novel approach for reducing the traveled distance of unscrewed aerial vehicles (UAVs) to improve the data collection efficiency in WSNs with poor node density. Yet the method that has been developed is aimed at increasing 'the efficiency and efficacy of data collection... with the assistance of Unmanned Aerial Vehicles (UAVs)' which means this work reveals practical application solutions. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Multidimensional Voronoi Constellations vs. Short Blocklength Probabilistic Shaping: A Comparison for Multilevel Coding Approach

Multidimensional Voronoi Constellations vs. Short Blocklengt...

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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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