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Study on Active Defense of Honeypot-Based Industrial control network 23

Study on Active Defense of Honeypot-Based Industrial Control...

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23rd IEEE International conference on High performance Computing and Communications, 7th IEEE International conference on Data Science and systems, 19th IEEE International conference on Smart City and 7th IEEE International conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Wei, Xunsi Yang, Dequan Beijing Institute of Technology School of Computer Beijing100081 China Network Information Technology Center Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9781665494571

With the rapid development of information, network security and information have played an increasingly important role in national production. As a result, the environment and application of industrial control networks are under more and more network security threats. Furthermore, most applications of industrial control networks belong to critical infrastructure, so to guarantee the security of industrial control networks has become more significant, and the defense of it is one of the essential issues in need of being solved. This paper focuses on studying the active protection for the security of industrial control networks and discusses the role of honeypot in the network security for the defense strategy of the industrial control network. Firstly, this paper introduces the theoretical knowledge of honeypots. Secondly, it analyzes the existing problems and development prospects of the honeypot. And finally, under the background of the industrial control network, it designs the SSH honeypot based on Transmission control Protocol/internet Protocol (TCP/TP), which is divided into two modules, including SSH information collection and alarm. This system can deploy based on the Docker of Linux. Through simulation practice, it proves that honeypot can effectively capture the attackers' attack and make an early warning to improve network security. © 2021 IEEE.

关键词： Industrial control Linux TCPIP Production network security Critical infrastructure

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Blockchain-based Authentication Scheme for Vehicle network Nodes 3

Blockchain-based Authentication Scheme for Vehicle Network N...

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3rd International conference on Blockchain Technology and Information Security, ICBCTIS 2023

作者： Di, Xiaoqiang Sun, Yitong Lu, Junchu Dai, Weiyi Qi, Hui Changchun University of Science and Technology Jilin Province Changchun China

ISBN: (纸本)9798350340402

internet of Vehicles (IoV) has developed into a useful service platform between vehicles and other vehicles, vehicles and roadways, and vehicles and pedestrians as a result of advancements in communication technology. However, along with the intelligent and networked IoV, IoV security issues are increasingly emerging, which often lead to extremely serious consequences. For example, when a vehicle is remotely attacked and maliciously controlled, it can lead to user privacy leakage. Attackers can also control the driving of vehicles through attacks, and there may even be a large number of connected cars being controlled in bulk, ultimately causing major social security incidents. In the internet of Vehicles, authentication technology is one of the main solutions to address the security issues of the internet of Vehicles. Many researchers have conducted some research on the authentication technology of vehicular network, but the traditional authentication scheme under the centralized system has problems such as frequent single-point failures and high overhead. When blockchain technology is properly implemented to the vehicle network environment, its security outperforms that of centralized systems due to features like decentralization, tamper-proof, security, and traceability. Because of this, we construct an optimization technique based on the Practical Byzantine Fault Tolerance (PBFT) algorithm in this work and present an efficient IoV decentralized authentication scheme based on the consensus algorithm of blockchain technology. After performance analysis, it is proved that our scheme has better performance and higher security. © 2023 IEEE.

关键词： Blockchain

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Adaptive Discrete-Time Sliding Mode control Design for Buck Converters with Disturbances 42

Adaptive Discrete-Time Sliding Mode Control Design for Buck ...

引用

42nd Chinese control conference, CCC 2023

作者： Liu, Peng Wang, Zuo School of Automation Nanjing210096 China Ministry of Education Key Laboratory of Industrial Internet of Things & Network Control Chongqing400065 China Shenzhen Research Institute Southeast University Shenzhen518063 China

ISBN: (纸本)9789887581543

In this paper, an adaptive discrete-time sliding mode controller (ADSMC) is investigated for buck converter systems with multiple external disturbances and parameter uncertainties. To improve the tracking accuracy and reduce the range of the quasi-sliding mode domain (QSMD), the second-order difference of disturbance estimations are introduced into the controller, which are provided by a discrete disturbance observer. Furthermore, an adaptive reaching law is implemented instead of constant gain to alleviate the control input chattering. Both dynamic and steady-state performance analyses are presented. Finally, simulation results are illustrated to demonstrate the control performance. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： controllers

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Optimization of Neural networks using Swarm Intelligence Techniques for Achieving Energy Efficiency in Smart Building Architecture 1

Optimization of Neural Networks using Swarm Intelligence Tec...

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1st IEEE International conference on Cognitive Robotics and Intelligent systems, ICC - ROBINS 2024

作者： Raja, M. Karthick Shermatova, Makhbuba Al Ansari, Mohammed Saleh Abdufattokhov, Shokhjakhon Rao, Vuda Sreenivasa Raj, I. Infant Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Turin Polytechnic University in Tashkent Civil Engineering and Architecture Department Tashkent Uzbekistan University of Bahrain College of Engineering Department of Chemical Engineering Bahrain Turin Polytechnic University Automatic Control and Computer Engineering Dept Italy Tashkent Intl. University of Education Dept of Information Technologies Tashkent Uzbekistan Koneru Lakshmaiah Education Fou. Green Fields Department of Computer Science and Engineering A.P Vaddeswaram522302 India K. Ramakrishnan College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy621 112 India

ISBN: (纸本)9798350372748

The increasing prevalence of smart building architectures, driven by the integration of internet of Things (IoT) devices and automation systems, has led to a surge in energy consumption. This research explores the application of swarm intelligence techniques as an innovative approach to optimize neural networks, aiming to strike a balance between maintaining the desired performance levels and minimizing energy consumption. The study investigates the integration of swarm-based optimization algorithms, such as Particle Swarm Optimization (PSO) into the training and operation of neural networks. These algorithms enable the networks to dynamically adapt and optimize their parameters in response to changing environmental conditions and user requirements. The research focuses on developing a comprehensive framework that considers the specific challenges posed by smart building architectures, including real-time data processing, sensor integration, and adaptive control. The proposed approach aims to achieve optimal neural network configurations that minimize energy consumption while ensuring reliable and responsive operation of smart building systems. The results demonstrate the potential of swarm intelligence to significantly improve the energy efficiency of neural network-enabled smart building architectures, providing a promising avenue for sustainable and intelligent infrastructure. The proposed model has an accuracy of 98.23% which is 7.64% higher than that of the traditional approaches. © 2024 IEEE.

关键词： Energy efficiency

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Meta-Verse VICA: AI Counselor Initiating Efficient and Safe Remote Service Support in Meta-Verse 17

Meta-Verse VICA: AI Counselor Initiating Efficient and Safe ...

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17th International conference on Signal-Image Technology and internet-Based systems, SITIS 2023

作者： Taniguchi, Yoshio Knauf, Rainer Fujikawa, Hiroshi Tsuruta, Setsuo Sakaki, Koji Suzuki, Nabuharu Ihara, Hirokazu Hitachi Industry & Control Solutions Ltd. Tokyo Japan School of Computer Science and Automation Ilmenau Germany Maruka Robotics Inc. Tokyo Japan Tokyo Denki University Tokyo Japan Adin-Lab Tokyo Japan

ISBN: (纸本)9798350370911

This paper proposes a framework for efficient remote service support in meta-verse. This work was initiated by a empathy-driven sensitive listening counselor bot enhanced by real and virtual services including digital twins selected by a powerful need/offer matching. This matching is leveraged on ADN (Autonomous Decentralization network) IBN (Intention Based network) for efficiency (high performance) exploiting DLT (Distributed Ledger Technology) for safety. This framework makes it possible to provide highly efficient teleservice including telemedicine in virtual spaces, which enables eternal evolution. Moreover, this paper introduces a concept for an integrated learning and evolutionary self-improvement of our metaverse system. © 2023 IEEE.

关键词： Distributed ledger

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Portable Executable Header Based Ransomware Detection using Power Iteration and Artificial Neural network 6

Portable Executable Header Based Ransomware Detection using ...

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6th International conference on Information systems and Computer networks, ISCON 2023

作者： Singh, Mahendra Pratap Karkhur, Yash National Institute of Technology Karnataka Surathkal Department of Computer Science and Engineering Mangaluru575025 India

ISBN: (纸本)9798350346961

In the present world, the dependency on different devices connected to the internet is increasing at a rapid rate day by day. Devices like smart watches, mobile phones, personal computers, etc., are part of our day-to-day life. We rely on them for most of our daily tasks. Since these devices are frequently used, they contain users' personal information and other essential data. More and more people use the internet due to emerging technology and intelligent devices, increasing the risk of misusing confidential information and other user-specific crucial data. With the development of cryptocurrency, Ransomware is one of the emerging attacks that prevent authentic users from accessing systems, resources, or data and enables adversaries to control access to such information. This paper presents an Artificial Neural network (ANN) based model that uses the 'Power Iteration' method and Portable Executable (PE) Headers to detect various types of Ransomware. We analyze the performance of the proposed model by experimenting with a dataset created using the PE files collected from multiple sources and demonstrate its better detection capability.. © 2023 IEEE.

关键词： Neural networks

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performance Enhancement of the Healthcare System Using Google Cloud Platform 1

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4th International conference on Advances in Distributed Computing and Machine Learning, ICADCML 2023

作者： Mohanty, Subhadarshini Dash, Alka Mohapatra, Subasish Sahoo, Amlan Mondal, Subrota Kumar Odisha University of Technology and Research Odisha Bhubaneswar India Macau University of Science and Technology Av. Wai Long Contai China

ISBN: (数字)9789819912032

ISBN: (纸本)9789819912025

Recent technology's ongoing evolution is unexpectedly altering many industries. A widely held idea states that the combination of scientific internet of Things (IoT) devices would cause a significant shift, particularly in the healthcare sector. In the implementation of vital medically accepted hardware, software, and connections, the usage of scientific IoT devices has repercussions. This report seeks to identify upcoming standard performance challenges by studying current IT cloud services. As a result, there are now new community performance demands, and there is a need for brand-new community performance standards and recommendations that are focused on network-related, scientific IoT devices, their users, and the associated cloud services in integrated home care. The internet of Things (IoT) produces plenty of statistics. Scientific sensor and actuator statistics can be remotely saved and analyzed using distributed servers thanks to cloud computing. The cloud version for data storage and analysis is becoming increasingly unable to handle the data avalanche, and processing is being pushed out to the edge of the network where the data-producing devices are. Region computing and fog computing are data management architectures that can unload data from the cloud, a method close to the patient, and communicate data from tool to tool or from tool to person in seconds or milliseconds. Fog computing (with nearby nodes) and location computing can be used to process sensor statistics close to the detecting and actuating devices (withinside the sensing devices). Fog computing and location computing, when compared to cloud computing, offer five advantages: faster data transmission, much less reliance on constrained bandwidths, more privacy and security, and greater control over data generated in foreign nations where judicial recommendations may also moreover limit the use or allow unwanted governmental access, and lower costs because more sensor-derived data is used. © 20

关键词： internet of things

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Comprehensive Evaluation Method of AC/DC Distribution System with Distributed Generation 4

Comprehensive Evaluation Method of AC/DC Distribution System...

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4th Power System and Green Energy conference (PSGEC)

作者： Li, Junhui Wang, Wenzhong Lu, Jiancong Tang, Zilin Ju, Hao Ai, Peng Dongguan Power Supply Bur Co Ltd Dongguan 523000 Peoples R China Tsinghua Sichuan Energy Internet Res Inst Chengdu 610213 Peoples R China

ISBN: (纸本)9798350365573;9798350365580

The traditional distribution network has gradually evolved into a sophisticated AC/DC distribution system with numerous power electronics components, control equipment, and distributed generation sources. Conducting a comprehensive evaluation of this system using advanced modeling, simulation techniques, and risk assessment theory is of paramount importance. Consequently, this paper establishes a comprehensive evaluation framework for AC/DC networks incorporating distributed generation. Initially, power flow and reliability models were developed to evaluate power quality levels and reliable power supply capabilities. Building upon these models, quantitative evaluation metrics for AC/DC systems were established, forming a layered evaluation index system comprising target, performance, and specific indicators. Furthermore, an integrated assessment approach utilizing fuzzy evaluation techniques was implemented to quantify the impact of distributed generation integration within AC/DC systems. The proposed evaluation model was tested and validated using the modified IEEE RBTS-6 main feeder F4 test case.

关键词： AC/DC distribution system distribution generations power quality reliability Power flow fuzzy comprehensive evaluation

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Investigating the Influence of Timer Forecasting on Transmission Efficiency of TCP Connections in Dense Muli-Hop Wi-Fi Links 2

Investigating the Influence of Timer Forecasting on Transmis...

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2nd IEEE International conference on Device Intelligence, Computing and Communication Technologies, DICCT 2024

作者： Joseph Auxilius Jude, M. Dinesh, V.C. Sri Yaalini, M. Sherwin San Markus, M. Sneha, A. Subhika, S. Kongu Engineering College Ece Perundurai India

ISBN: (数字)9798350372847

ISBN: (纸本)9798350372847

Multi-hopping remains an effective solution in wireless scenarios with highly populated user strength. The internet protocol, especially the transmission control protocol (TCP) protocol, plays a huge role in the performance of systems with wireless links. The performance of TCP is heavily dependent upon the working of its timers. The TCP implementation largely trusts the retransmission timeout (RTO) timer algorithm that forecasts the packet delivery duration on a network connection. In multi-hop Wi-Fi links, RTO forecasting tumbles during impulsive round-trip time (RTT), which arises due to intermediate node re-routing and MAC retransmission. This experimental study intends to assess the effectiveness of different RTO algorithms and their impact on TCP connection transmission efficiency and latency characteristics in multi-hop Wi-Fi links. From the analysis at higher hop counts, CC-RTO obtained 35.12% and 17.2% by hybrid RTO. This study also examines the RTO's limits, like lack of accuracy in RTO estimation, and provides suggestions for lowering forecast errors in multi-hop Wi-Fi environments. © 2024 IEEE.

关键词： Transmission control protocol

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Enhancing Power Efficiency Prediction in Small-Scale Data Centers using Attention-Based Deep Neural networks 5

Enhancing Power Efficiency Prediction in Small-Scale Data Ce...

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5th IEEE International conference on Architecture, Construction, Environment and Hydraulics, ICACEH 2023

作者： Saranraj, Kumaravel Liu, Chuan-Ming National Taipei University of Technology College of Electrical Engineering and Computer Science Taipei Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350303605

The growth of internet-based applications, the rise of enormous data storage, and the evolution of internet of Things (IoT) technologies have significantly increased the number of data centers. They are crucial in various industries for storing data either on-site, off-campus, or both. Temperature control is crucial in these buildings, as they house a large number of running machines. Several factors affect their performance, including heat generated by IT equipment. Data centers consume a sizable amount of electricity due to the complexity of big data workloads that require extensive computing. The IT infrastructure side of data centers accounts for 60% of power consumption, while systems for heating, ventilating, and air conditioning (HVAC) consume the remaining 40%. To improve energy efficiency, Power Usage Efficiency (PUE) is used to evaluate and enhance the energy performance of data centers. In this study, a prediction model was developed using data from EnergyPlus and IoT sensors, focusing on neural net-based machine learning models such as artificial neural network (ANN), deep neural network (DNN), long short-term memory (LSTM), and attention-based LSTM (Att-LSTM) to predict power usage effectiveness (PUE) values. The accuracy of these algorithms was evaluated using real data from IoT sensors in a data center, and by applying a comparative strategy, the Att-LSTM model outperformed other models. By utilizing Att-LSTM models, data center operators can optimize energy consumption and enhance overall performance, providing valuable insights into improving energy efficiency in data centers. © 2023 IEEE.

关键词： internet of things

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