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Developing Simulation Capabilities for Supply Chain Cybersecurity of the Electricity Grid

Developing Simulation Capabilities for Supply Chain Cybersec...

引用

ieee PES conference on Innovative Smart Grid Technologies Latin America (ISGT-LA)

作者： Keller, Joseph Paul, Shuva Hutto, Kevin Grijalva, Santiago Mooney, Vincent J., III Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30345 USA

ISBN: (纸本)9798350336962

The electricity grid has evolved into a cyber-physical system that integrates electric systems with advanced information and operation technology. With the frequency of cyber-incidents increasing in recent years, the need for secure and robust cybersecurity solutions for supply chain networks for critical infrastructures, such as the electricity grid, has intensified. This paper proposes a testbed that involves hardware-in-the-loop simulation of power system operations and real-time data acquisition and management using the OSIsoft PI System and a common set of power control devices. A discussion of the development of this simulator's capabilities for supply-chain cybersecurity is included. The simulator is used to emulate the operation of a remote terminal unit, an SEL 751 relay, a vendor, a utility, and an attacker to execute the power system adversarial operation in a power grid environment. The testbed can provide researchers with a streamlined method to assess the impact of adding security measures related to the supply chain to the electricity power grid.

关键词： Supply chain cybersecurity real-time data acquisition and management intelligent electronic devices

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Enriching Recommender systems Results with data about Sustainability and Ethical Standards of Brands 22

Enriching Recommender Systems Results with Data about Sustai...

引用

22nd ieee/WIC international conference on Web Intelligence and intelligent Agent Technology (WI-IAT)

作者： Cossatin, Angelo Geninatti Mauro, Noemi Ardissono, Liliana Univ Torino Comp Sci Dept Turin Italy

ISBN: (纸本)9798350309188

In recommender systems research, not only user preferences but also the sustainability and ethical standards of the services underlying item fruition should be considered to promote virtuous selection decisions. We analyze user interfaces that guide item comparison taking these evaluation criteria into account and we test a synchronized multi-list aimed at raising users' awareness about items by enabling them to (i) sort items according to different evaluation criteria, and (ii) simultaneously view the overall evaluation of an item and its ranking in each criterion. A user study in the fashion domain has shown that the presentation of data about environmental sustainability and ethical standards induces virtuous selection behavior and that participants are more confident in their selections when using our synchronized multi-list user interface than with single-lists.

关键词： multi-list user interfaces sustainability fashion human-centric computing and services

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Integrating IoT for Enhanced Flood Monitoring: A Raspberry Pi Pico-based System with Real-time data Visualization and Early Warning Alerts

Integrating IoT for Enhanced Flood Monitoring: A Raspberry P...

引用

2024 international conference on Innovative computing, intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Jerrin Simla, A. Aranganathan, A. Vedanarayanan, V. Gomathi, T. Sivasundarapandian, S. Sakthi Prabha, R. Department of Cse Chennai India Sathyabama Institute of Science and Technology Department of Ece Chennai India Hkbk College of Engineering Department of Cse Bangalore India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543617

This paper outlines the development of an innovative flood monitoring system using IoT technology, Raspberry Pi Pico, and various sensors to track conditions near reservoirs. Placed in flood-prone areas, the hardware unit integrates a Wi-Fi module (ESP8266) for data transmission, while sensors measure critical parameters such as level, temperature, humidity, and flow. The collected data is displayed on an LCD screen and simultaneously transmitted to an IoT web application. The system employs the ThingSpeak web application to securely store data in a private channel. In the occurrence of a flood, the web application promptly alerts relevant authorities and the public. This comprehensive flood monitoring system aims to significantly improve early warning capabilities, providing timely information to enhance disaster preparedness and response strategies. The integration of IoT technology with real-time data visualization contributes to effective flood management and mitigation efforts. © 2024 ieee.

关键词： Wi-Fi

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Driving Safety Monitoring and Warning for Connected and Automated Vehicles via Edge computing 25

Driving Safety Monitoring and Warning for Connected and Auto...

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ieee 25th international conference on intelligent Transportation systems (ITSC)

作者： Chang, Cheng Zhang, Kunpeng Zhang, Jiawei Li, Shen Li, Li Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Tsinghua Univ Dept Automat BNRist Beijing 100084 Peoples R China Tsinghua Univ Dept Civil Engn Beijing 100084 Peoples R China

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

In this paper, we systematically study how to use edge computing to monitor the movements of multiple connected and automated vehicles (CAV) and warn of potential accidents (e.g., lane departures, collisions). Compared to conventional approaches that only use the sensing data of individual vehicles, cooperative vehicle infrastructure systems directly collect the movement data of vehicles via vehicle-to-everything (V2X) communications and thus easily calculate the risk of every vehicle synthetically. We propose a fast algorithm and the corresponding data structure model to calculate collision risks based on the timely received data. We also discuss the data accuracy and transmission delay requirements to guarantee the driving safety of CAVs. Testing results show the effectiveness of the proposed approach.

关键词： Time-frequency analysis Computational modeling Prediction algorithms data structures data models Safety Trajectory

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Detection of data Leakage Based on DNS Traffic 5

Detection of Data Leakage Based on DNS Traffic

引用

5th ieee international conference on Power, intelligent computing and systems, ICPICS 2023

作者： Zhang, Siyu Han, Zhipeng Jiang, Kaida Network and Information Center Shanghai Jiao Tong University Shanghai China School of Electronic Information and Electronic Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798350333442

In recent years, malicious software that utilizes covert channels to transmit private data has become increasingly prevalent. The privacy information of affected users, such as accounts and passwords, is quietly transmitted to attackers. Hackers commonly employ covert tunnels that utilize the Domain Name System (DNS) to achieve this, transmitting stolen information through malicious domains. Traditional DNS tunneling techniques generate significant communication traffic, and related research has delved deeper into this area. However, they face challenges in detecting malicious software that transmits data at low rates. This paper proposes a DNS data leakage detection algorithm based on anomaly detection models. Through experimental verification combining simulated attacks with real data, this algorithm demonstrates high detection rates for low-rate data leakage channels, while also maintaining good detection performance for traditional DNS tunnels. © 2023 ieee.

关键词： Anomaly detection

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Enhanced Bearing Fault Diagnosis Using Transfer Learning and Spectrograms with Pretrained GoogLeNet

Enhanced Bearing Fault Diagnosis Using Transfer Learning and...

引用

2024 international conference on Telecommunications and intelligent systems, ICTIS 2024

作者： Bouchareb, Narimane Bengherbia, Billel Chadli, Samir Tobbal, Abdelhafid Rebiai, Mohamed Toumi, Yacine LSEA University Yahia Fares of Medea Laboratory of Advanced Electronics Systems Medea Algeria

ISBN: (纸本)9798331527396

With the increasing complexity of modern machinery, there is a rising demand for advanced intelligent algorithms capable of diagnosing faults in rotating equipment through vibration signal analysis. In response, we introduce a fault diagnosis method based on transfer learning, utilizing a pretrained GoogLeNet model and spectrogram representations generated from vibration signals. By using the Case Western Reserve University (CWRU) bearing dataset, raw vibration data is converted into spectrograms to capture the time-frequency attributes of the signals. These spectrograms are then input into the pretrained GoogLeNet, which was originally developed for large-scale image recognition, to extract deep features crucial for fault detection. Through transfer learning, the model leverages the pretrained network's ability to recognize complex patterns, significantly improving fault classification accuracy while minimizing the need for extensive labeled data and training time. Experimental results confirm that our approach surpasses traditional methods, delivering enhanced diagnostic performance on the CWRU dataset. This research underscores the effectiveness of applying pretrained convolutional neural networks combined with spectrograms for accurate and efficient machinery fault diagnosis. © 2024 ieee.

关键词： Convolutional neural networks

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Experimental Analysis of Learning Based Approach to Evaluate the Integration of Social Networking Platform with Internet of Things Technology

Experimental Analysis of Learning Based Approach to Evaluate...

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2024 international conference on Innovative computing, intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Karunakaran, A. Venkatraman, C.B. Suganthi, P. Santhi, G.B. Alok, Narkhede Shalout, Imad S. A. Engineering College Department of Ece Chennai India Sona College of Technology Department of Eee Salem India Sri Sai Ram Institute of Technology Department of Cse Chennai India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa university Faculty of engineering Department of Civil Engineering Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Enterprising entrepreneurs are forging a new paradigm for consumer-business interactions by leveraging the convergence of social networking platforms-such as Facebook and Twitter-and the Internet of Things (IoT). This paper aims to assess an integration pipeline's performance using a learning based approach. Our aim is to understand the complex interplay between data that has been collected from IoT and how social media users act using advanced methods in data analytics. Our approach involves gathering data from both SNPs and the Internet of Things (IoT) and pre-processing it as needed before building a model that can be predicted and its performance evaluated. As opposed to the proposed method Elevated Social Networking Integration (ESNI) is a better strategy than the existing Social Networking Integration Analyzer (SNIA), which permits collecting less information through connected devices to enhance decision-making on consumer behavior and encounters. By offering a methodical approach to evaluating the value proposition of combining IoT with SNP, this study adds to the corpus of knowledge. The framework facilitates seamless interaction across diverse social media platforms, optimizing data flow and user experience. Empirical testing demonstrates ESNI's effectiveness in boosting engagement metrics and user satisfaction compared to conventional integration methods. This research highlights ESNI's potential to redefine social networking strategies, providing a robust foundation for future innovations in digital communication and marketing. © 2024 ieee.

关键词： data assimilation

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A GUI For OBO Foundry's ROBOT Library To Encourage Usability and Adoption 12

A GUI For OBO Foundry's ROBOT Library To Encourage Usability...

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12th ieee international conference on Healthcare Informatics (ieee-ICHI)

作者： Liu, Luke Zhang, Yinglun McGinty, Hande Kucuk Amith, Muhammad Univ Washington Seattle WA USA Kansas State Univ Dept Comp Sci Manhattan KS USA Univ Texas Med Branch Dept Biostat & Data Sci Galveston TX 77555 USA

ISBN: (纸本)9798350383744;9798350383737

Since its inception in 2013, OBO ROBOT has become a known ontology development tool to manage and develop new ontologies. OBO ROBOT is available as a command line executable requiring some learning curve to install and use. To provide accessibility for advanced tools to service novice ontology developers, we introduce a GUI frontend for OBO Foundry's ROBOT, a known ontology development tool. Our initial work is available on GitHub and was developed in a cross-platform framework (QT C++) to enable portability across different operating systems. Future work will include enlisting potential users to evaluate accessibility and usability.

关键词： knowledge engineering usability semantic web ontology biomedical informatics

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Individual Privacy Protection in Cybersecurity Using data Analytics and Machine Learning

Individual Privacy Protection in Cybersecurity Using Data An...

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2024 international conference on Innovative computing, intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Arun, M. Bhavadharani, K. Supreeth, B.R. Pundir, Sumit Pradeepa, K. Razak, K. Abdul Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Cse Tamil Nadu Chennai India V.S.B. Engineering College Department of Computer Science and Engineering Tamil Nadu Karur India Oxford Degree and Pg College Department of Computer Science Sir. M. VisveshwaraiahLayout Ullal Bangalore India Graphic Era Deemed to be University Department of Computer Science and Engineering Uttarakhand Dehradun India Kpr College of Arts Science and Research Department of Artificial Intelligence and Data Science Tamil Nadu Coimbatore India K. Ramakrishnan College of Engineering Department of Mathematics Tamil Nadu Trichy India

ISBN: (纸本)9798331543617

Traditional cybersecurity addresses struggle to keep up with the ever-changing nature of cyber threats and usually fail to detect new privacy breaches. The paper proposes a proactive protection mechanism that employs data analytics and machine learning (ML) to promote individual privacy in the digital realm. The system analyzes several data sources, including system events, network traffic, and user activity logs, using advanced ML methods like Random Forests (RF), Support Vector Machines (SVM), and Neural Networks (NN), to detect and prevent privacy violations. Ensemble learning techniques, feature engineering, and real-time monitoring enable adaptability in the face of shifting threats. According to the results, the proposed system surpassed the existing system in terms of accuracy (0.92), recall (0.89), precision (0.91), and F1 score (0.90). Its ability to detect unlawful access, unusual logins, and irregular data requests is proved by anomaly detection rates utilizing One-Class SVMs (91%) and Isolation Forest (89%). The system's low false positive rate of 5% demonstrates how effectively it can detect privacy breaches, resulting in stronger cybersecurity and greater privacy protection for both individuals and enterprises. © 2024 ieee.

关键词： Cybersecurity

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Multi-Agent systems for Autonomous IoT Network Management Using Distributed Reinforcement Learning 3

Multi-Agent Systems for Autonomous IoT Network Management Us...

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3rd international conference on intelligent systems, advanced computing, and Communication, ISACC 2025

作者： Neelamegam, G. Venkatesan, Rajaram Ramya, S.R. Ramya, R.S. Akshya, J. Sundarrajan, M. Choudhry, Mani Deepak VSB College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Builders Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Tirupur India Dr NGP Institute of Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Tamil Nadu Coimbatore India SRM Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Chennai India SRM Institute of Science and Technology Department of Networking and Communications Tamil Nadu Chennai India SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai India

ISBN: (纸本)9798331523893

The ever-growing complexity of IoT networks ignited by their wide scale adoption in applications such as smart cities, the industrial automation, and health care, compelled to develop sophisticated yet resource efficient and scalable network management solution. Centralized systems are traditional in architecture, which leads to higher latency and bottlenecks, while decentralized systems can distribute workloads however the lack of adaptability to real time, along with the difficulty of extracting maximum resource usage, are limitations. In recent years, reinforcement learning (RL) based approaches have taken shape as a promising alternative to facilitate the automated learning and optimisation of task management. However, there are two challenges for existing RL based methods: slow convergence, inefficient priority assignment of tasks, and poor coordination amongst agents in the case of dynamic, large scale IoT environment. To solve these challenges, in this research we propose a distributed reinforcement learning based multi agent framework for managing IoT network. The framework also integrates a task prioritization mechanism to dynamically choose an optimal task allocation solution. Further validation was performed in a simulated IoT environment where real world inspired datasets were used. The proposed framework demonstrated 30% more task throughput than centralized, 25% less latency than decentralized, and 40% more energy efficiency than current RL based systems. The system was further validated using advanced visualizations such as anomaly detection maps and resource allocation efficiency graphs, demonstrating its capacity to manage dynamic load of tasks and resource allocation. The proposed framework addresses the critical limitations of current methods, through scalable, adaptive, and energy efficient framework for managing IoT network, and accordingly helps advancing the intelligent and autonomous IoT systems. © 2025 ieee.

关键词： Resource allocation

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