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A Bayesian Belief Network Framework for Protecting Generative AI-driven Attack in Smart Grid Communication

A Bayesian Belief Network Framework for Protecting Generativ...

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2025 International Conference on Computing, Networking and Communications, ICNC 2025

作者： Munir, Md Shirajum Proddatoori, Sravanthi Muralidhara, Manjushree Shetty, Sachin Univesity of West Georgia School of Computing Analytics and Modeling CarrolltonGA30118 United States Old Dominion University Dept. of Computer Science NorfolkVA23529 United States Old Dominion University Dept. of Ece NorfolkVA23529 United States

ISBN: (纸本)9798331520960

In the era of artificial intelligence (AI), native generative AI (GenAI) has become prominent in generating intelligent cyber-attacks in a smart grid environment by imitating operational parameters and metrics. As a result, it is imperative to understand the attack generation capabilities of native GenAI models, such as Generative Adversarial Networks (GANs) and Autoencoders in a smart grid environment. This research aims to explore the potential of GenAI models to accurately imitate and analyze the behavior of cyberattacks within a smart grid environment. The goal is to understand the risks posed by these intelligent attacks while developing strategies to protect against disruptive Distributed Energy Resources (DER) events, such as incorrect load shifting, imbalanced demand supply, and unstable price forecasting. First, this work leverages two customized native GenAI models, namely GAN and Autoencoder, to synthesize DERs control message parameters such as nominal power consumption, price elasticity coefficients, and communication data packets, among others, to introduce highly unpredictable new intelligent attack vectors. Second, this research proposes and develops a new Bayesian Belief Networks (BBNs) framework by creating correlation dependency nodes among DER control message parameters to understand the risks of intelligent attack vectors better while monitoring and mitigating their impact on smart grid operations. In particular, by leveraging the customized BBN, the system can observe and analyze the uncertain behaviors in DER operations that GenAI introduces, and the proposed framework can help mitigate grid vulnerabilities by understanding the poisonous parameters through mutual information. Finally, the experiment results show that the Autoencoder outperformed GAN in reproducing intelligent attacks with an MSE about 98.984% lower than that of GANs. Additionally, BBN can explain prominent parameters of the intelligent attacks' by quantifying dependencies thro

关键词： Signaling

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Enhancing Cybersecurity in Sustainable Energy: Regulatory Compliance, Challenges, and Policy Innovations 1

Enhancing Cybersecurity in Sustainable Energy: Regulatory Co...

引用

1st International Conference on Sustainable Energy Technologies and Computational Intelligence, SETCOM 2025

作者： Saxena, Shruti Saxena, Shivani Tahilramani, Nikunj Patel, Ujjawal Talreja, Vandana P. Patel, Ashish National Forensics Sciences University Dept. of School of Cyber Security & Digital Forensics Gandhinagar India Institute of Advanced Research Dept. of Computer Sciences & Engineering Gandhinagar India Adani Institute of Digital Technology Management Dept. of Big Data Analytics Gandhinagar India R.C.Technical Institute Electrical engg.dept Ahmedabad India Ibm India

ISBN: (纸本)9798331520540

The global switch to renewable energy is both a necessity for sustainable development and climate change mitigation. However, this transition is coupled with unique cyber security issues because of the greater reliance on networked digital systems. This paper identifies significant flaws in the current cyber security regulations and procedures while highlighting their effectiveness in the renewable energy sector. Recent research with the analysis highlighted the necessity of adaptable regulatory tools and the particular vulnerabilities that exist in renewable energy networks. According to the report, stronger regulations on the policymaking side must be enforced: rigorous regulatory compliance, increased robustness, and the incorporation of newer security practices that address changing threats. We explore how the integration of cutting-edge technology such as artificial intelligence (AI) could strengthen system resilience using current case studies like China's renewable energy projects and Germany's Energiewende. Furthermore, the importance of cybersecurity in the attainment of the Sustainable Development Goals (SDGs) is evaluated based on how it supports environmental conservation, economic development, and renewable energy. This research will aid the stakeholders and policymakers in making a safe and sustainable energy future. © 2025 IEEE.

关键词： Clean energy

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Exploring the Potential of IoT in Healthcare: Challenges and Opportunities 3

Exploring the Potential of IoT in Healthcare: Challenges and...

引用

3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Verma, Navneet Rana, Sumit Gaba, Shivani Nagpal, Shally Paniapt Institute of Engineering and Technology Dept. of Computer Science & Engg. Haryana Panipat India Paniapt Institute of Engineering and Technology Dept. of Artificial Intelligence and Data Science Haryana Panipat India

ISBN: (纸本)9798350342338

Due to the propagation of IoT technologies Healthcare cannot remain outside of this paradigm. In latest years, there has been a lot of interest in the Internet of Things (IoT) technology to relieve the strain on healthcare systems brought on by the ageing population and the rise in persistent illnesses. One of the main obstacles preventing advancement in this field is standardization or global connectivity between the Internet of Things (IoT) and the medical surroundings. All from electrical engineers to data engineers have a significant problem in having to integrate everything in a global setting. The difficulties that healthcare IoT confronts, such as security, privacy, and less-energy procedures, are discussed, and suggestions are provided for future research paths. This paper is written to gather data on various Internet of Things architectures now used in Healthcare and analyse how well different academics have applied it. © 2023 IEEE.

关键词： Architecture Connectivity Healthcare Internet of Things Security

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Medication Management Application for Directly Observed Treatment of TB Patients 6

Medication Management Application for Directly Observed Trea...

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6th International Conference on Electrical Engineering and Information and Communication Technology, ICEEICT 2024

作者： Tania, Marzia Hoque Rahman, Md. Asib Ahmed, Kadir Uddin Centre for Big Data Research in Health Unsw Sydney Sydney Australia Buet Dept. Computer Science and Engineering Dhaka Bangladesh Independent Researcher Sydney Australia

ISBN: (纸本)9798350385779

Tuberculosis (TB) poses global health challenges, requiring efficient management and monitoring of patients undergoing Directly Observed Therapy (DOT). This paper introduces TB-meds, a proposed software application to automate essential aspects of TB patient management. TB-meds aims to optimize resource utilization, improve patient care, and enhance TB control efforts. The application streamlines medication distribution, facilitates medication adherence monitoring, sends timely clinical test requirement's notifications, and enables convenient treatment status updates. By automating these processes, TB-meds has the potential to enhance efficiency, improve treatment outcomes, and reduce transmission rates. Its user-friendly interface promotes seamless communication among healthcare providers and TB patients. With tasks automation, the workload of DOT providers can be reduced, and more time can be dedicated to patient education, nutrition counseling, and lifestyle guidance. Additionally, TB-meds proposes to revolutionize TB surveillance by enhancing data collection and analysis capabilities, enabling informed decision-making and targeted interventions. The implementation of TB-meds holds promise for transforming TB patient management, contributing to better health outcomes, and advancing TB control efforts. © 2024 IEEE.

关键词： Telemedicine

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Comparative Analysis of Python-Based Machine Learning Algorithms for Stock Market Prediction: A Case Study on Forecast Accuracy and Methodological Insights 1

Comparative Analysis of Python-Based Machine Learning Algori...

引用

1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Rathore, Rachna Swetha, B. Saritha, P.S. Kumar, Ajay Singh, Vijay Vir Shyamu Jiwaji University Dept. of Mathematics Gwalior India Meenakshi A.H. Edu. and Research Dept. of Mathematics Chennai India Dhanalakshmi Srinivasan College Dept. of AI and Data Science Coimbatore India Manglayatan University Dept. of Computing and IT Aligarh India Lingayas Vidyapeeth Dept. of Mathematics Faridabad India J.S. University Dept. of Computer Science Shikohabad India

ISBN: (纸本)9798350356816

Predicting stock market movements is a complex task due to the high volatility and unpredictability of stock prices. In recent years, machine learning algorithms have proven to be effective tools for addressing this challenge. This study presents a detailed analysis of various Python-based machine learning techniques applied to stock market prediction. It examines the effectiveness of several algorithms in forecasting stock prices and evaluates their comparative performance. The findings reveal that utilizing machine learning methods can significantly improve the accuracy of stock market predictions. This research offers valuable insights for both researchers and practitioners aiming to leverage machine learning for stock market forecasting. By providing a comprehensive overview of different techniques and their applications, the study serves as a critical resource for those seeking to enhance predictive capabilities and make informed decisions in the dynamic and unpredictable environment of stock market analysis. © 2024 IEEE.

关键词： Adversarial machine learning

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Real-Time Stress Level Monitoring Using IOT

Real-Time Stress Level Monitoring Using IOT

引用

2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Asha Patil, Nandini Preeti, B.M. Laxmi Sharnbasva University Dept. of Artificial Intelligence and Data Science Karnataka Kalaburagi India Sharnbasva University Dept. of Computer Science and Engineering Karnataka Kalaburagi India Sharnbasva University Dept. of Electrical and Electronics Engineering Karnataka Kalaburagi India

ISBN: (纸本)9798350315455

Stress is a prevalent and debilitating issue in contemporary society, contributing to a range of physical and mental health problems. As our lives become increasingly fastpaced and interconnected, addressing stress effectively has become imperative. This introduces a pioneering approach to tackle challenge through real-time stress level monitoring utilizing the Internet of Things (IoT). We propose a holistic system that merges IoT sensors, advanced data analytics, and machine learning techniques to provide continuous stress assessment and management solutions. The data is collected through the designed model then preprocessed, and essential features are extracted to construct a predictive model. The heart of the system is a machine learning model, which constantly updates itself based on user-specific stress patterns, yielding highly accurate real-time stress level estimations. Users are provided with actionable feedback and personalized recommendations, ranging from deep breathing exercises to lifestyle adjustments. Extensive experimentation demonstrates the system's robustness, with stress level predictions boasting a mean absolute error of under 5%. © 2023 IEEE.

关键词： data preprocessing feature extraction health monitoring Internet of Things (IoT) machine learning personalized recommendations physiological sensors real-time stress monitoring well-being

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Expert Patient Interaction Language Model (EPILM)

Expert Patient Interaction Language Model (EPILM)

引用

Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Pratham Gala Pooja Vartak Jay Vithlani Naman Chheda Dept. of Computer Science and Engineering (Data Science) Dwarkadas J Sanghvi College of Engineering Mumbai India Dept. of Computer Science and Engineering (Data Science) Mumbai India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Today’s youth encounter numerous mental health challenges, many of which can be effectively addressed through professional intervention. However, there is a notable reluctance among young individuals to seek professional mental health services. The integration of language models in providing mental health support offers a non-intrusive and accessible approach for young people to engage with professional assistance for their mental health concerns. Although existing language models like GPT 3.5, Falcon-7B model have been effective, they also present certain challenges like high dependability of data from sources like Reddit, computational obstacles hindering access, and a preference for certain models over superior alternatives have impeded advancements in this area. that require thorough examination. Issues related to biases and the model’s performance across diverse scenarios underscore the necessity for more intelligent strategies. The proposed method involves enhancing the Falcon-7B model through the application of quantization low rank adaptation (Q-LoRA). This technique reduces the computational requirements and simplifies the accessibility for individuals equipped with the necessary technology while providing the average bertSCORE 85%. By addressing these issues, paper’s objective is to refine mental health support systems to benefit a broader audience.

关键词： Training Adaptation models Quantization (signal) Social networking (online) Computational modeling Graphics processing units Mental health Chatbots data models Tuning

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Optimizing Production and Inventory Management for Defective Items in Manufacturing Systems 3

Optimizing Production and Inventory Management for Defective...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Ahmed, Khandakar Rabbi Hossain, Arafat Asif, MD Amaddus Bepary Mohammad, Mahathir Rahaman, Mustafizur Dewan, MD Arafat Miyan Research Institute International University of Business Agriculture and Technology Dhaka Bangladesh Central Michigan University Dept. of Business Data Analytics Mt Pleasant United States Southern California State University Dept. of Business Administration and Management Los AngelesCA United States Southern California State University Dept. of Business Administration and Management United States Westcliff University Dept. of Cloud Engineering IrvineCA United States Los Angeles City College Dept. of Computer Science Los Angeles United States

ISBN: (纸本)9798331523893

Manufacturing defective items is one of the challenges for production efficiency and inventory control. Conventional approaches like Economic Order Quantity (EOQ) and Just-in-Time (JIT) often fall short of the flexibility needed to respond to quality variations in real time. It promotes defect detection and control by employing machine learning (ML) for inventory management. Four classifiers were tested using the UCI SECOM dataset (Bagging, AdaBoost, Gradient Boosting, and Random Forest) to identify defective items. We used effective preprocessing techniques to boost model performance, such as SMOTE for class balancing and PCA for reducing dimensionality and standardization. The bagging and random forest classifiers produced 96.6% and 96.9% test accuracies, with good precision and recall scores. Within this context, we propose an Adaptive Inventory Management Framework comprising machine learning algorithms incorporating predictive analytics to reduce waste, optimize production scheduling(s), and improve resource allocation. These results are consistent with defining Industry 4.0 strategies and show that ML has the potential to transform defect management and inventory optimization in manufacturing systems. © 2025 IEEE.

关键词： Industry 4.0

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Designing of Creator's Information Hub using Machine Learning 2

Designing of Creator's Information Hub using Machine Learnin...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Gabhane, Diksha Gawande, Ujwalla Gaikwad, Sanjana Gajarlewar, Swati Bhawalkar, Rita Yeshwantrao Chavan College of Engineering Dept. of Computer Science & Design Nagpur India Yeshwantrao Chavan College of Engineering Dept. of Information Technology Nagpur India NCPharmacy Wanadongri Nagpur India G H Raisoni College of Engineering and Management Dept. of Cyber Security and Data Science Pune India

ISBN: (纸本)9798331528713

The proliferation of educational content on platforms like YouTube has transformed digital learning, yet it presents challenges in effective creation, dissemination, and impact assessment. This paper proposes a strategic initiative to collaborate with 6,000 active educational YouTubers to enhance global educational outreach. Central to this initiative is the compilation and analysis of a comprehensive dataset containing vital information about content creators and their audiences. Leveraging advanced machine learning algorithms, the analysis aims to identify collaboration opportunities, optimize engagement strategies, enhance content relevance and impact, facilitate knowledge sharing, and measure outcomes. The problem statement highlights fragmentation in educational content, lack of data-driven insights, and suboptimal collaboration opportunities. Addressing these challenges requires a structured approach to connect creators, leverage data analytics, and establish a collaborative ecosystem for enriching educational experiences worldwide. © 2024 IEEE.

关键词： Adversarial machine learning

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Deep Neural Networks in Social Media Forensics: Unveiling Suspicious Patterns and Advancing Investigations on Twitter 3

Deep Neural Networks in Social Media Forensics: Unveiling Su...

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3rd Intelligent Cybersecurity Conference, ICSC 2023

作者： Sharrab, Yousef Al-Fraihat, Dimah Alsmirat, Mohammad Isra University Dept. of Data Science and Artificial Intelligence Amman Jordan Isra University Dept.of Software Engineering Amman Jordan University of Sharjah Dept. of Computer Science Sharjah United Arab Emirates

ISBN: (纸本)9798350382174

Text data forensics, a rapidly developing field that focuses on analyzing textual content to identify criminal or suspicious activities, is becoming increasingly important due to the popularity and the huge number of text posts on social media platforms. This study aims to improve the detection of suspicious text on social media using deep neural networks. Suspicious text is defined as any text that is likely to be associated with criminal activity or is unusual or out of the ordinary. This study could make a significant contribution to the field of text data forensics by helping to improve the detection of such text. We leveraged the 'CIC Truth Seeker dataset 2023' [1], which is widely recognized as a comprehensive and representative dataset for text data forensics research. The dataset contains over 180,000 tweets related to 700 real and 700 fake pieces of news, labeled by experts. In this study, we enhance text data forensics in social media by leveraging the powerful analytical capabilities of deep neural networks. More specifically, we investigate the effectiveness of Long Short-Term Memory (LSTM) in the detection of suspicious text. The results are very promising as we achieved an accuracy of 96% during preliminary evaluations. We plan to explore future work on the model's potential applications, including criminal activity identification, misinformation detection, and online harassment prevention. © 2023 IEEE.

关键词： Sentiment analysis

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