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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 ...

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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

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

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

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

Cooperative Control of Vehicle Platoons with Unknown Dynamic...

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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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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...

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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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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...

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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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Observer-based self-organizing adaptive fuzzy neural network control for non-linear, non-affine systems with unknown sign of control gain and dead zone: A case study of pneumatic actuators

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND control 2022年第11期44卷 2214-2234页

作者： Mawlani, Peyman Arbabtafti, Mohammadreza Shahid Rajaee Teacher Training Univ Shabanloo St Tehran *** Iran

In this paper, an observer-based self-organizing adaptive fuzzy neural network (OSOAFNN) control for non-linear, non-affine systems with the unknown sign of control gain and dead zone is presented. First, a reverse dead-zone compensator scheme to cope with the impact of dead-zone phenomenon existing in control input is investigated. Then, an observed-based control approach to address immeasurable states of the system is proposed, utilizing this approach all states of the system are not needed to be available. A self-organizing fuzzy neural network (SOFNN) technique is presented to approximate the non-linear and unknown function of the observer error dynamics. The proposed fuzzy neural network (FNN) model benefits from two main advantages: (1) the number of rules is automatically generated or pruned and (2) the parameters of antecedent and consequent part of SOFNN are updated through the hybrid tuning, simultaneously. Furthermore, the control law contains a Nussbaum function which deals with the unknown sign of control gain. As the system states are immeasurable, the strictly positive real (SPR) Lyapunov function to guarantee the closed-loop system stability, and tracking error convergence to zero is employed, as well as the boundedness of control parameters are assured through a projection law merged with adaptive law. Finally, the controller is practically implemented on a non-linear, non-affine pneumatic system with unknown dead zone which exploits just a sensor for output measuring. Experimental results show that the proposed controller has satisfactory performance in tracking different trajectories, and tracking error for desired signal case I and case ii is limited to [-1.5, 1.5] and [-1,1]mm, respectively.

关键词： Non-linear and non-affine systems observer-based control self-organizing fuzzy neural network reverse dead-zone compensator Nussbaum function SPR Lyapunov function pneumatic actuators

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Process Bases for Ensuring Interoperability and Synchronization of Multifunctional network-Centric systems

Process Bases for Ensuring Interoperability and Synchronizat...

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2024 systems of Signal Synchronization, Generating and Processing in Telecommunications, SYNCHROINFO 2024

作者： Kozlov, S.V. Kubankov, A.N. Federal Research Center 'Computer Science and Control' of the Russian Academy of Sciences Moscow Russia Moscow Technical University of Communications and Informatics Moscow Russia

ISBN: (纸本)9798350373448

The article discusses the process aspects of the complex problem of interoperability in the creation and operation of automated control systems as multifunctional integrated systems based on network-centric principles. It is noted that the integration of functional systems vertically (in terms of including several levels of the management hierarchy) and horizontally of interaction (in terms of expanding the variety of combined functional systems), as their complexity increases, leads to a rapid increase in the dimension of the network-centric system, which, in turn, expands the subject area of the problem of interoperability of its constituent functional systems and at the same time increases the complexity of the analysis of its problem area. On the other hand, in the field of general management, there is a tendency to move from automation of functional tasks to automation of management processes that combine the performance of several interrelated tasks, and, ultimately, to automation of organizational processes (business processes). It is noted that the need to apply a process approach to the development of network-centric systems as integrated multifunctional systems is determined, on the one hand, by the complexity of the traditional functional approach to ensuring the interconnected implementation of an increasing number of management tasks using network-centric systems as a qualitatively new high-tech infocommunication basis and, on the other hand, the actual transition from automation of (most often disconnected) management tasks to automation of business processes. The necessity of clarifying the composition of the process system in the life cycle of network-centric systems with the allocation of complete groups of target functional processes and supporting processes of system engineering is shown. The ontology of the subject and problem areas of the network-centric system as an integrated management system is presented and an integrated approach to the app

关键词： Interoperability

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Prioritized Phase Split Optimization for Coordinated Traffic Signal control in Urban network Using Deep Reinforcement Learning 26

Prioritized Phase Split Optimization for Coordinated Traffic...

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IEEE 26th International conference on Intelligent Transportation systems (ITSC)

作者： Kim, Hyunsoo Jin, Zhixiong Tak, Hyeyoung Yu, Hwapyeong Yeo, Hwasoo Korea Adv Inst Sci & Technol Dept Civil & Environm Engn Daejeon South Korea Univ Gustave Eiffel ENTPE LICIT ECO7 Lyon France Urban Transport Syst Lab LUTS Ecole Polytech Fed Lausann EPFL Ecublens Switzerland

ISBN: (纸本)9798350399462

In traffic signal control (TSC), Deep Reinforcement Learning (DRL) has demonstrated its superiority to traditional approaches. However, there remain two challenges in DRL-based traffic signal control. How to cooperatively control decentralized intersection agents and how to apply TSC methods to the real world are still open. To address the aforementioned challenges, we propose a novel DRL-based strategy that optimizes the phase split for each intersection. We prioritize each intersection for signal control and adjust its phase split in accordance with its assigned priority. To be more specific, we start by optimizing the phase split of the intersection with the highest priority and then control the intersection with the following priority based on the outcome from the first intersection. In this manner, the coordinated optimization is taken into account. Additionally, we maintain the pre-defined order and definition of phases, and a fixed cycle time during the optimization process to support practical application. The proposed method yields an improvement in the network efficiency, ranging from 8-16%, compared to the traditional method when tested in real urban network scenarios. This study also tests the impact of the control priority on the model performance.

关键词： Reinforcement learning

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Optimizing Energy Saving for Wireless networks via Offline Decision Transformer 59

Optimizing Energy Saving for Wireless Networks via Offline D...

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59th Annual IEEE International conference on Communications (IEEE ICC)

作者： Xu, Yi Tian Wu, Di Jenkin, Michael Jang, Seowoo Liu, Xue Dudek, Gregory Samsung AI Ctr Montreal PQ Canada Samsung Elect Suwon South Korea

ISBN: (纸本)9781728190549

With the global aim of reducing carbon emissions, energy saving for communication systems has gained tremendous attention. Efficient energy-saving solutions are not only required to accommodate the fast growth in communication demand but solutions are also challenged by the complex nature of the load dynamics. Recent reinforcement learning (RL)-based methods have shown promising performance for network optimization problems, such as base station energy saving. However, a major limitation of these methods is the requirement of online exploration of potential solutions using a high-fidelity simulator or the need to perform exploration in a real-world environment. We circumvent this issue by proposing an offline reinforcement learning energy saving (ORES) framework that allows us to learn an efficient control policy using previously collected data. We first deploy a behavior energy-saving policy on base stations and generate a set of interaction experiences. Then, using a robust deep offline reinforcement learning algorithm, we learn an energy-saving control policy based on the collected experiences. Results from experiments conducted on a diverse collection of communication scenarios with different behavior policies showcase the effectiveness of the proposed energy-saving algorithms.

关键词： Offline reinforcement learning energy saving on/off control 5G cellular network

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RedTE: Mitigating Subsecond Traffic Bursts with Real-time and Distributed Traffic Engineering 24

RedTE: Mitigating Subsecond Traffic Bursts with Real-time an...

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ACM SIGCOMM conference

作者： Gui, Fei Wang, Songtao Li, Dan Chen, Li Gao, Kaihui Min, Congcong Wang, Yi Tsinghua Univ Beijing Peoples R China Zhongguancun Lab Beijing Peoples R China BNRist Beijing Peoples R China Guangdong Commun & Networks Inst Guangzhou Peoples R China Southern Univ Sci & Technol Inst Future Networks Shenzhen Peoples R China

ISBN: (纸本)9798400706141

Internet traffic bursts usually happen within a second, thus conventional burst mitigation methods ignore the potential of Traffic Engineering (TE). However, our experiments indicate that a TE system, with a sub-second control loop latency, can effectively alleviate burst-induced congestion. TE-based methods can leverage network-wide tunnel-level information to make globally informed decisions (e.g., balancing traffic bursts among multiple paths). Our insight in reducing control loop latency is to let each router make local TE decisions, but this introduces the key challenge of minimizing performance loss compared to centralized TE systems. In this paper, we present RedTE, a novel distributed TE system with a control loop latency of < 100ms , while achieving performance comparable to centralized TE systems. RedTE's innovation is the modeling of TE as a distributed cooperative multi-agent problem, and we design a novel multi-agent deep reinforcement learning algorithm to solve it, which enables each agent to make globally informed decisions solely based on local information. We implement real RedTE routers and deploy them on a WAN spanning six city datacenters. Evaluation reveals notable improvements compared to existing solutions: < 100ms of control loop latency, a 37.4% reduction in maximum link utilization, and a 78.9% reduction in average queue length.

关键词： Traffic Engineering network Optimization Machine Learning

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