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检索条件"任意字段=IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems"

共 34828 条记录，以下是21-30 订阅

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An Adaptive Golden Jackal Optimization Algorithm for Mobile Robotic Path Planning

An Adaptive Golden Jackal Optimization Algorithm for Mobile ...

引用

2024 international conference on Mechatronics and intelligent control, ICMIC 2024

作者： Chen, Siwen Zheng, Xiujuan Shanghai United Imaging Healthcare Co. Ltd. Shanghai China Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan University of Science and Technology Wuhan China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Given the shortcomings of the traditional Golden Jackal Optimization (GJO) algorithm, including limited accuracy and slow convergence speed in solving mobile robot path planning problems, an improved adaptive Golden Jackal Optimization (IAGJO) method is proposed. Firstly, a nonlinear adaptive energy strategy is presented to adjust the energy decay pattern, balancing global and local search. Secondly, an enhanced position update mechanism based on Cauchy and Gaussian mutation increases population diversity and guide the search based on optimal individuals, thereby facilitating efficient exploration of unknown regions and avoiding local optima. Finally, the IAGJO algorithm is applied to mobile robot path planning (MRPP), demonstrating that the IAGJO achieves shorter path lengths and higher search efficiency, exhibiting significant advantages over existing methods. © 2025 SPIE.

关键词： Optimization algorithms

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A Real-World Dataset "IDSIoT2024" for Machine Learning/Deep Learning based cyber Attack Detection System for IoT Architecture 3

A Real-World Dataset "IDSIoT2024" for Machine Learning/Deep ...

引用

3rd international conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Koppula, Manasa Leo Joseph, L.M.I. Dept. of ECE SR University Warangal India

ISBN: (纸本)9798331527549

The Internet of Things (IoT) is an emerging technology evaluating its inception in all domains like industries, home automation, healthcare, agriculture, etc. The major challenge in IoT is securing the IoT devices and information associated with the IoT devices from hackers or unauthorized persons. These cyber security attacks can be detected early for timely interventions and reduce major risks. Machine Learning (ML)/Deep Learning (DL) has become an influential technique for automatic Attack or Intrusion Detection systems (IDS), putting forward numerous advantages over traditional techniques. A well-structured, real-time IoT security dataset must be used to develop IDS using ML/DL approaches. The article proposes a Real-World dataset IDSIoT2024 consisting of 16,230,955 records to help researchers advance solutions for tackling cyber security in IoT networks. The records presented in the dataset were captured using the Wireshark tool in a real-world IoT architecture. The data collection has taken two months from the middle of June 2023 to August 2023. The proposed dataset can be employed in the field of Artificial Intelligence (AI) to develop ML/DL approaches and detect cyber-attacks in IoT networks. A large amount of data needs to be trained to an ML algorithm to get precise detection of attacks. Therefore, the proposed dataset with a vast number of records with different attacks can accommodate the researchers for the development of Intrusion Detection systems. © 2025 ieee.

关键词： cyber attacks

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Secure Remote State Estimation Against False Data Injection Attacks Based on Improved Kalman Filtering Algorithm 24

Secure Remote State Estimation Against False Data Injection ...

引用

2nd international conference on Frontiers of intelligent Manufacturing and automation, CFIMA 2024

作者： Zhuang, Xu Wang, Zhichuang He, Wei School of Intelligence Science and Technology University of Science and Technology Beijing Beijing China

ISBN: (纸本)9798400710681

Due to the development of wireless networks, many secure remote state estimation algorithms have been studied by researchers. This paper proposed an improved Kalman filter method to deal with the secure remote state estimation problem caused by the false data injection (FDI) attacks in cyber–physical systems (CPSs). We redesign Kalman filter gain by making full use of the attack information so that the systems can achieve accurate state estimation even if the FDI attack exists. Moreover, the modelling of the attack is arbitrary Gaussian distribution thus have more extensive applications. The Kullback-Leibler divergence is used to measure the impact of the attacks. No matter the attack parameters are time-varying or time-invariant, they can be identified and used to redesign Kalman filter gain. Finally, comparative simulations are provided to verify the effectiveness and superiority of our method. © 2024 Copyright held by the owner/author(s).

关键词： Kalman filters

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A Power-voltage coordinated control strategy of inverters in low-voltage distribution networks

A Power-voltage coordinated control strategy of inverters in...

引用

2024 international conference on Mechatronics and intelligent control, ICMIC 2024

作者： Wei, Yinzhong Mei, Jianchun Zhou, Yalong Su, Jiabin Yan, Anxin Zhou, Zhen China Energy Engineering Group Jiangsu Power Design Institute Co. Ltd. Nanjing211102 China Jiangsu Keneng Electric Power Engineering Consulting Co. Ltd. Nanjing210000 China School of Automation Nanjing University of Science and Technology Nanjing210094 China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Considering the problem concerning voltage beyond limit caused by the photovoltaic generators in low-voltage distribution network system, a power-voltage coordinated manage method about inverters is offered to deal with voltage beyond limit by photovoltaic generators. This method can give priority to voltage adjustment through reactive power belonging to the PV, if the reactive regulation ability from the PV reaches its limit while the voltage continues to exceed the limit, an active power reduction control based on sparrow search algorithm is further adopted to minimize the waste of photovoltaic active power and avoid voltage beyond limit. The simulation outcomes confirm the potency as well as advantages belonging to the offered method in dealing with the beyond-limits about voltage created by the PV. © 2025 SPIE.

关键词： Photovoltaics control systems Lithium Solar energy Resistance automation Mathematical optimization Algorithm development Design Safety

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Research on Mechanical automation control based on Bidirectional Long Short-Term Memory 1

Research on Mechanical Automation Control based on Bidirecti...

引用

1st international conference on intelligent systems and Computational Networks, ICISCN 2025

作者： Song, Xiaolu Zaozhuang Vocational College of Science & Technology College of Mechanical and Electrical Engineering China

ISBN: (纸本)9798331529246

The rapid advancement of industrial automation has led to increased reliance on intelligent control systems for optimizing mechanical processes. Traditional automation methods often struggle with dynamic environmental conditions, system uncertainties, and real-time decision-making challenges. Many existing approaches, such as PID controllers and conventional neural networks, fail to effectively handle sequential dependencies in time-series data, resulting in suboptimal system performance. To address these limitations, this research presents an advanced mechanical automation control framework based on a Bidirectional Long Short-Term Memory (BiLSTM) algorithm. The BiLSTM model enhances automation efficiency by capturing long-range dependencies in sequential data, improving predictive accuracy and system adaptability. By leveraging bidirectional processing, the model effectively learns past and future context, leading to superior performance in control precision and response time. Experimental evaluations demonstrate that the proposed framework achieves an accuracy of 98.2%, significantly outperforming conventional automation control techniques in terms of robustness, adaptability, and computational efficiency. This study contributes to the development of intelligent, data-driven mechanical automation systems that align with Industry 4.0 objectives and enhance operational reliability. © 2025 ieee.

关键词： Three term control systems

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Knowledge-Defined Internet of Things Architecture for Cloud-Edge Collaborative Industrial Monitoring 24

Knowledge-Defined Internet of Things Architecture for Cloud-...

引用

2nd international conference on Frontiers of intelligent Manufacturing and automation, CFIMA 2024

作者： Zhou, Hongyu Xing, Fangyuan Tong, Fei Wang, Mufeng Zhang, Yan Yang, Shuaifeng Southeast University Nanjing China China Industrial Control Systems Cyber Emergency Response Team Beijing China

ISBN: (纸本)9798400710681

The Industrial Internet of Things (IIoT) has become a crucial technology driving the transformation of manufacturing towards intelligence. Traditional industrial networks primarily focus on data collection, whereas emerging IIoT emphasizes intelligent management and optimized control utilizing advanced technologies such as big data and artificial intelligence. With the more complex application scenarios, sole reliance on cloud computing fails to meet requirements for low latency and high security, thus leading to a trend towards edge-cloud collaborative computing. Knowledge-Defined Networking (KDN) introduces a knowledge plane above the control and data planes, which utilizes machine learning and deep learning methods to extract and analyze data and can guide the control plane in updating and optimizing network management and control decisions. This architecture enables the system to understand domain knowledge, supports knowledge-based reasoning decisions, and facilitates higher-level intelligent applications. This paper proposes a Knowledge-Defined Edge-Cloud Collaborative IoT architecture tailored for industrial monitoring scenarios. Therein, the edge is responsible for initial real-time data processing, while the cloud handles large-scale data storage and complex analytics. Knowledge definition are introduced into the entire framework and enables a comprehensive understanding of equipment status through historical data analysis and real-time feedback, which facilitates predictive issue identification and optimization of production plans. The enabling technologies that can implement the functions of the knowledge-based cloud, knowledge-based edge, knowledge-based end, and interfaces are summarized, which are expected to provide guidance for future IIoT. © 2024 Copyright held by the owner/author(s).

关键词： Cloud computing architecture

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intelligent control algorithms for industrial automation systems

Intelligent control algorithms for industrial automation sys...

引用

2025 ieee international Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Sarathkumar, D. Anil, A.R. Sivadasan, J. Jayakumar, T. Manivel, Murugesan Anandhakumar, C. Engineering Kongu Engineering College Department of Electrical and Electronics Tamil Nadu 638 060 India Sree Buddha College of Engineering Pattoor Department of Computer Science &engineering Kerala Alappuzha India Psr Engineering College Department of Electronics and Communication Engineering Tamil Nadu Sivakasi626140 India Nandha Engineering College Department of Electrical and Electronics Engineering Tamil Nadu 638052 India Karpagam Institute of Technology Department of Electrical and Electronics Engineering Tamilnadu Coimbatore641021 India Sri Ramakrishna Institute of Technology Department of Electrical and Electronic Engineering Tamil Nadu Coimbatore641010 India

ISBN: (纸本)9798331529833

Industrial automation has become a cornerstone of modern manufacturing, enhancing efficiency, reliability, and scalability. The integration of intelligent control algorithms, such as fuzzy logic, neural networks, genetic algorithms, and model predictive control, has revolutionized traditional automation systems, enabling more adaptive, efficient, and robust operations. This paper explores the application and impact of these intelligent control techniques in industrial settings. It highlights how machine learning algorithms can predict equipment failures, optimize processes, and enhance decision-making in real-time. Case studies are presented to illustrate the benefits and challenges of deploying these algorithms across various automated systems, such as robotic assembly lines, process control in chemical plants, and smart warehousing. Furthermore, the paper discusses the integration of these algorithms with industrial IoT (IIoT) platforms, facilitating seamless communication and data exchange between machines and control systems. The research also addresses the challenges of implementing intelligent control, such as computational complexity, security concerns, and the need for real-time processing. Finally, this study evaluates future trends, emphasizing the potential of combining artificial intelligence and deep learning techniques with industrial automation for a more adaptive and autonomous manufacturing ecosystem. © 2025 ieee.

关键词： Genetic algorithms

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Deep Learning to Effectively Detect and Eliminate Real-Time Network Intrusions 3

Deep Learning to Effectively Detect and Eliminate Real-Time ...

引用

3rd international conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Harikesh, S. Sujatha, G. Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu Chennai India

ISBN: (纸本)9798331527549

Network traffic has been growing exponentially for the past several years. As a result, it has become more and more difficult to detect cyber intrusions - especially in unbalanced environments, where the attacks are usually covered up by normal traffic. This investigation is set to deal with the problem by coming up with an intrusion detection system that uses a deep learning approach to effectively find and eliminate the unbalanced network intrusions. The process includes data preprocessing for balancing the classes, feature engineering for choosing optimal features, and comparison of CNN and RNN models for improving detection accuracy. The tests have shown precision of 93% and recall of 88% which in total brought 99.8% accuracy. Also, the real-time deployment has demonstrated the model’s ability to dynamically traffic patterns, which induced false positives to be reduced remarkably. The results help in the improvement of intrusion detection systems by providing a scalable, adaptive, and efficient way to handle constantly altering cyber threats. © 2025 ieee.

关键词： Network intrusion

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DTS: Dynamic Traffic Scrubbing Against Link Flooding Attacks with Programmable Data Plane

DTS: Dynamic Traffic Scrubbing Against Link Flooding Attacks...

引用

2024 international conference on Mechatronics and intelligent control, ICMIC 2024

作者： Zhao, Zheng Liu, Fang Meng, Zhaoran Zhao, Qi Xie, Xin Dalian Maritime University Dalian China Northeastern University Shenyang China Hunan University of Information Technology Changsha China Hunan Province Higher Education Key Laboratory of Intelligent Sensing and Computing Changsha China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Link flood attacks (LFA) represent a significant cybersecurity threat due to their ability to cause extensive network disruptions. Traditional traffic scrubbing methods, relying on static rules, often struggle to counter dynamic LFAs effectively. This paper introduces the dynamic traffic scrubbing (DTS) method, leveraging programmable data planes. DTS integrates moving target defense principles into traffic scrubbing, dynamically deploying and adjusting scrubbing rules to thwart attackers. Game theory is employed to analyze the distributed denial of service attack and defense strategies. Experimental results demonstrate that DTS effectively mitigates dynamic LFAs with minimal overhead, showcasing its robustness in defending against sophisticated cyber threats. © 2025 SPIE.

关键词： Denial-of-service attack

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cyberbullying Prediction using K-Means Clustering with Learning Methodologies for Emotion Detection in Tweets 3

Cyberbullying Prediction using K-Means Clustering with Learn...

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3rd international conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Privietha, P. Sasirekha, R. Melwin Akash, L. Punitha, K. Marshal Felix, A. Ramyabharathi, R. Department of CSE Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331527549

The rise of social media has led to an increase in real-time data that can reveal potential cyber threats. This research explores the use of emotion detection in tweets to predict cyber attacks, employing K-Means Clustering in combination with machine learning methodologies. By analyzing emotional trends, such as fear, anger, and anxiety, this study establishes a correlation between social media sentiment and cyber attack occurrences. The results demonstrate the potential for early warning systems that harness emotional patterns for cybersecurity. Social media platforms like Twitter provide a wealth of real-time data that can reveal societal trends, including indicators of potential cyber threats. This study investigates the use of emotion detection in tweets as a predictive tool for cyber attack forecasting. By employing Natural Language Processing (NLP) techniques, emotional states such as fear, anger, and anxiety are extracted and analyzed. K-Means Clustering is utilized to group similar emotional patterns, enabling the identification of significant trends. These clusters, combined with historical cyberbullying data, are used as features in supervised learning models for cyber threat prediction. The results demonstrate the effectiveness of integrating unsupervised clustering and machine learning to correlate spikes in negative emotions with the likelihood of cyber incidents. This research highlights the potential for proactive cybersecurity strategies that leverage social media sentiment as an early warning system. © 2025 ieee.

关键词： Tweets

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