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High Interaction Attack Defense Mechanism Based on RASP Technology in Power System network Security Reinforcement

High Interaction Attack Defense Mechanism Based on RASP Tech...

引用

2024 International conference on Electrical Power systems and Intelligent control, EPSIC 2024

作者： Sun, Qiang Xiao, Chi Fan, Chunlei Rong, Junxing Yang, Ranxing Li, Roufei State Grid Shandong electric power company weihai power supply company Shandong China State Grid Shandong Electric Power Company Weihai Wendeng District Power Supply Company Shandong China

ISBN: (纸本)9798350366075

This article explores the high interaction attack defense mechanism based on runtime application self-protection (RASP) technology in power system network security reinforcement. With the rapid development of information technology, network security has become a key factor affecting national security and social stability. This article focuses on a high interaction attack defense mechanism that can monitor the execution process of applications in real-time. This mechanism is good at identifying and mitigating potential attack behaviors, thereby improving the security and reliability of the power system. The experimental results show that the performance of the mechanism has been significantly improved, while reducing the resource consumption rate and false alarm rate to the optimal values of 94.9 points, 10.2%, and 5%, respectively. This study provides innovative solutions for network security protection in the field of power systems. ©2024 IEEE.

关键词： High Interactive Attack Defense Mechanism network Security Power System Runtime Application Self-Protection

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A Novel Convolutional Neural network Architecture with a Continuous Symmetry 1

引用

3rd CAAI International conference on Artificial Intelligence (CICAI)

作者： Liu, Yao Shao, Hang Bai, Bing Tsinghua Univ Beijing Peoples R China Zhejiang Future Technol Inst Jiaxing Peoples R China

ISBN: (数字)9789819991198

ISBN: (纸本)9789819991181;9789819991198

This paper introduces a new Convolutional Neural network (ConvNet) architecture inspired by a class of partial differential equations (PDEs) called quasi-linear hyperbolic systems. With comparable performance on image classification task, it allows for the modification of the weights via a continuous group of symmetry. This is a significant shift from traditional models where the architecture and weights are essentially fixed. We wish to promote the (internal) symmetry as a new desirable property for a neural network, and to draw attention to the PDE perspective in analyzing and interpreting ConvNets in the broader Deep Learning community.

关键词： Convolutional Neural networks Partial Differential Equations Continuous Symmetry

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Next-Generation Orchestration: Quantum Computing for network Services

Next-Generation Orchestration: Quantum Computing for Network...

引用

12th IEEE International conference on Computer, Information and Telecommunication systems (CITS)

作者： Zeydan, Engin Blanco, Luis Mangues-Befalluy, Josep Arslan, Suayb S. Turk, Yekta Ctr Tecnol Telecomunicac Catalunya CTTC Barcelona 08860 Spain MIT Cambridge MA 02139 USA

ISBN: (纸本)9798350359107;9798350359091

As the demand for high-performance and dynamically adaptable network services continues to grow, the convergence of quantum computing and network orchestration represents a promising development. This paper explores the transformative potential of quantum computing in the orchestration of network services and introduces new paradigms that transcend classical limitations. Using a case study, we propose a workflow for an exemplary link failure process for generative AI (GenAI) and quantum computing-based service orchestration in an edge cloud (EC) network. Later, we address the challenges of integrating services based on quantum computing (using a quantum search algorithm to select the best connections) with GenAI-based topology control and management in an orchestration framework. Our work also introduces multi-solution quantum search for orchestrating services across different edge clouds, emphasizing efficiency and accuracy. Algorithmic considerations, including the impact of the oracle and hybrid approaches, are also thoroughly explored. We conclude the paper with potentials and comparisons of possible GenAI techniques and quantum search algorithms in terms of their advantages and disadvantages.

关键词： Quantum computers

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A Reinforcement Learning Method in Cooperative Multi-Agent System for Production control System 2

A Reinforcement Learning Method in Cooperative Multi-Agent S...

引用

2nd IEEE International conference on Data Science and network Security, ICDSNS 2024

作者： Malathy, V. Al-Jawahry, Hassan M. Madhura, G.K. Suganya, G. Rashmi, P. Sr University Department of Ece Warangal India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Dr. Sns Rajalakshmi College of Arts and Science Department of Computer Science Coimbatore India Inventuriz Labs Private Ltd Bengaluru India

ISBN: (纸本)9798350373110

Nowadays, in various domains including distributed control, telecommunication, robotics and economics to address the problems, Multi-Agent systems (MAS) is used. To solve with preprogrammed agent behavior complexity of many tasks of these domains making it difficult. The multi- agent systems can limit problem of control in complex system production, to solve more efficiently. However, local optimization tendencies are often shown in iterative development algorithms. So, by using reinforcement learning and considering indicators of global key performance this paper presents a new method for cooperative multi-agent system. To cooperative order agents, for the purpose of this a central deep and double deep learning module its knowledge is being transferred. From the results, subsequent reinforcement learning using memory, the order's experience is stored and increases 50% of average mean cycle. In comparison of production control conventional methods, nonlinear production system and parameters of learning interdependencies between the characteristics are investigated and the performance is evaluated. © 2024 IEEE.

关键词： Deep reinforcement learning

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A Novel Collaborative Heterogeneous Supervision network for Small Object Detection Method Based on Panchromatic and Hyperspectral Images

A Novel Collaborative Heterogeneous Supervision Network for ...

引用

2024 IEEE International conference on control Science and systems Engineering, ICCSSE 2024

作者： Li, Yuan Kong, Ziyang Xu, Qizhi School of Mechanical and Electrical Engineering Beijing Institute of Technology Beijing China

ISBN: (纸本)9798331517199

With the increasing availability of simultaneous panchromatic and hyperspectral images, object detection methods based on them have demonstrated significant application advantages. However, they still face several challenges that limit detection performance: 1) the sizes of small objects remain very small even in the fused images, insufficient texture information and spectral information that is easily confused, leading to lower accuracy in object detection;2) etection-by-Preprocess (DBP) methods often suffer from spectral and spatial detail distortions, compromising target features;3) Preprocess-free detection (PFD) methods extract panchromatic and hyperspectral features directly through networks, but the black-box nature of deep network makes it difficult to ensure precise alignment of these two types of features, thereby hindering further improvements in detection accuracy. Therefore, this paper proposed a novel Collaborative Heterogeneous Supervision network (CHS-Net) for small object detection on panchromatic and hyperspectral images. First, integrating fusion and detection components into a heterogeneous supervision network enhances learning capabilities by incorporating more empirical knowledge. Second, a unified joint regulation strategy was introduced to enhance integrated learning capabilities using optimized feedback loss functions. This approach enhanced the attention of different components to target features, effectively improving weak small target detection performance. Finally, comparative experiments based on EO-1 dataset demonstrate that the proposed method outperforms many start-of-the-art approaches. © 2024 IEEE.

关键词： Self-supervised learning

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17th International conference on Computational Intelligence in Security for Information systems, CISIS 2024 and the 15th International conference on EUropean Transnational Education, ICEUTE 2024

17th International Conference on Computational Intelligence ...

引用

17th International conference on Computational Intelligence in Security for Information systems, CISIS 2024 and the 15th International conference on EUropean Transnational Education, ICEUTE 2024

ISBN: (纸本)9783031750151

The proceedings contain 33 papers. The special focus in this conference is on Computational Intelligence in Security for Information systems. The topics include: An Approach Based on LLMs for Forensic Threat Detection in Autonomous systems;smart Water Meter network Anomaly and Failure Detection System;Design and Implementation of a Fast, Platform-Adaptive, AIS-20/31 Compliant PLL-Based True Random Number Generator on a Zynq 7020 SoC FPGA;A Comparison of Frameworks for Heterogeneous Computing Using High-performance Pattern-Matching for DNA Biometrics and Digital Forensics;Fuzzing Robotic Software Using HPC and LLM;performance Impact of Strengthening the Accountability and Explainability System in Autonomous Robots;Generation of Industrial Protocol-Traffic via Enhanced Wasserstein GAN;cyber Security in Hospitals: A network-Oriented Model for Behavioural Learning of Employees During Phishing Simulations;Augmenting API Security Testing with Automated LLM-Driven Test Generation;Exploring the Use of LLMs to Understand network Traces;securing V2X Communication: A Hybrid Deep Learning Approach for Misbehavior Detection;Use of GAN Models and CNN Heatmaps in Malware;Evaluating Sniffers, IDS, and IPS: A Systematic Literature Mapping;performance Analysis of NTT Algorithms;exploring the Landscape of Honeypots in the Fight Against Cyber Threats: A Systematic Mapping of Literature;phishing and Spam Prevention Powered by Jetson Nano;decentralized Edge-Based Detection of Label Flipping Attacks in Federated Learning;analysis of Post-Quantum Cryptographic Algorithms: A Systematic Literature Review;A Comparison of AI-Enabled Techniques for the Detection of Attacks in IoT Devices;understanding Malware Dynamics in IoT networks: Dataset Construction Using Mathematical Epidemiology and Complex networks;Quality of Service Enhancement for IoT-Based Smart Office System Using Ad Hoc On-Demand Distance Vector-Smart control Ration control Algorithm (AODV-SRCA);cyberattack Detector for Real-Time

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Research on the Design and performance Optimization of Intelligent Traffic Signal control System Based on Internet of Things 4

Research on the Design and Performance Optimization of Intel...

引用

4th Asia-Pacific conference on Communications Technology and Computer Science, ACCTCS 2024

作者： Cao, Yang Institute of Smart City and Intelligent Transportation Southwest Jiaotong University Sichuan Chengdu610000 China

ISBN: (纸本)9798350359985

With the rapid development of Internet of Things technology, intelligent traffic signal control systems have become an excellent way to improve traffic efficiency and safety. In this paper, we model an intelligent traffic signal control system based on the basic theory of the Internet of Things. It introduces a graph neural network and principal component analysis for performance optimization. Numerical examples include the relationship between the traffic flow prediction or signal control results in the sample library and the traffic flows, the relationship with devices in the Internet of Things, the relationship with graph neural networks, and the relationship with principal component analysis error. It turns out that an intelligent traffic signal control system based on the Internet of Things can more accurately predict traffic flow and optimize signal control using graph neural networks and principal component analysis. It works with the Internet of Things to realize intelligent traffic management. In this paper, we use theoretical analysis to elucidate the system's internal mechanisms and provide scientific support for further improving the quality of transportation systems and meeting users' expectations. © 2024 IEEE.

关键词： Graph neural networks

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BBR-SJ: Optimization of time delay jitter in GEO satellite network 24

BBR-SJ: Optimization of time delay jitter in GEO satellite n...

引用

3rd International conference on Signal Processing, Computer networks and Communications, SPCNC 2024

作者： Ren, Mingming Wang, Hongdong Huang, Cao Luo, Feiteng Xizang Minzu University Shaanxi Xianyang China Pervasive Communication Research Center Purple Mountain Laboratories Jiangsu Nanjing China

ISBN: (纸本)9798400710834

With the continuous advancement of communication technology, GEO (geostationary orbit) satellite has shown significant advantages in the field of long-distance communication, and will be an important part of the future satellite-earth converged communication architecture. In order to solve the problem of delay jitter in GEO satellite network using tcp protocol, we proposed BBR-SJ improvement protocol. BBR-SJ monitor the network time changes and dynamically adjust the transmission parameters, judging the degree of jitter based on the dispersion of delay data, and implementing corresponding rate control and congestion control strategy, effectively reduce the delay jitter on data transmission, and by extending the delay validity period detection window, reduce the error rate of the detection process. Simulation experiments show that BBR-SJ is excellent in reducing time delay jitter and improving transmission stability, effectively maintaining high-rate transmission and preventing network congestion, significantly enhancing the network performance, and providing a key technology for improving the service quality of satellite communication. © 2024 Copyright held by the owner/author(s).

关键词： Satellite communication systems

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Robust and Adaptive PID control Synthesis Based on Radial Basis Function Approach for Unmanned Aerial System

Robust and Adaptive PID Control Synthesis Based on Radial Ba...

引用

2024 IEEE Silchar Subsection Annual conference, SILCON 2024

作者： Priya, Parul Kamlu, Sushma S Birla Institute of Technology Mesra Department of Electrical & Electronics Engineering Ranchi India

ISBN: (纸本)9798331540821

This study introduces a robust and adaptive control synthesis mechanism for the attitude and position stabilisation of quadcopter unmanned aerial systems (UAS). Ensuring robustness in the face of uncertainties is crucial when designing a real-time control system. To precisely follow the trajectory, focus on fine-tuning flight controller uncertainty for optimal responsiveness and accuracy. In this work, the quadcopter UAS mathematical model is first obtained using the Euler-Newton approach to describe system dynamics and kinematics. Then, an optimised PID controller with the help of a Radial Basis Function Neural network (RBFNN) is developed. This is employed to reduce the complexity and issues of manual tuning of the PID controller parameters, besides possessing an adaptation mechanism for optimum control performance. Considering the nonlinear dynamics of quadcopters, we proposed a radial neural network-based basis function system to amend automatic PID gains, which has allowed the design of adaptable PID controller systems. As demonstrated by thorough simulation, it is clear from the results that the RBFNN-based adaptive PID control framework can significantly advance UAS precise tracking, especially in response time, stability, and robustness. © 2024 IEEE.

关键词： Radial basis function networks

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Cooperative control of Vehicle Platoons with Unknown Dynamics using Neural network-Enhanced Reinforcement Learning

Cooperative Control of Vehicle Platoons with Unknown Dynamic...

引用

2025 IEEE International conference on Mechatronics, ICM 2025

作者： Yazdani Bejarbaneh, Elham Du, Haiping School of Electrical Computer and Telecommunications Engineering University of Wollongong WollongongNSW2500 Australia

ISBN: (纸本)9798331533892

This study proposes an optimal cooperative control framework for heterogeneous vehicle platoons using a neural network (NN)-based reinforcement learning (RL) algorithm in an identifier-critic-actor framework. Typically, determining the optimal control policy requires solving the Hamilton-Jacobi-Bellman (HJB) equation, but the presence of nonlinear terms within the HJB equation makes deriving analytical solutions challenging. While RL algorithms can overcome this issue, existing NN-based RL approaches are inherently complex as their update rules are derived from gradient descent optimization of the squared approximation of the HJB equation. This complexity makes their implementation challenging for nonlinear multi-vehicle systems with unknown dynamics. The proposed control strategy for platooning leverages an NN-based RL algorithm, where the update rules are directly derived from the negative gradient of a positive function that is mathematically equivalent to the HJB equation. Additionally, an NN-based identifier is incorporated into the platooning control design to dynamically estimate unknown vehicle dynamics in real time. The methodology is experimentally validated using the high-fidelity Mixed Traffic Simulator (MiTaS) co-simulation platform, which combines the Simulation of Urban Mobility (SUMO) microscopic traffic simulator with MATLAB environment. Simulations results demonstrate the efficacy of the learned strategy in achieving optimal leader-tracking performance. © 2025 IEEE.

关键词： Optimal control systems

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