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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 34963 条记录，以下是31-40 订阅

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Integration of Process Optimization and automation: A Way to AI Powered Digital Transformation 1

Integration of Process Optimization and Automation: A Way to...

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1st international conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Kotte, Kulasekhara Reddy Thammareddi, Latha Kodi, Divya Anumolu, Venkateswara Rao Arun Kumar, K. Joshi, Shubham INC SLRI Solutions India India Cyber Security Senior Data Analyst Truist Financial India Technical Architect ERP Solutions- Information Technology India Big Data Architect India Bipin Tripathi Kumaon Institute of Technology Department of Computer Science & Engineering Almora India

ISBN: (纸本)9798331518578

One of the most important factors facilitating AI-powered digital transformation is the combination of process automation and optimisation. Organisations may improve operational efficacy, save costs, and streamline procedures by utilising cutting-edge technology like robotic process automation (RPA), machine learning (ML), and artificial intelligence (AI). While automation seeks to reduce human participation in repetitive and rule-based operations, process optimisation concentrates on improving current business processes to attain improved productivity. The development of intelligent, adaptable, and self-improving systems is made possible by the combination of these two methodologies. Predictive findings, real-time decision-making, and customised customer experiences are further made possible by AI-driven analytics. This connection promotes innovation, scalability, and agility across sectors, giving businesses a competitive edge in the quickly changing digital market. The article examines the main principles, advantages, and difficulties of this shift and provides a road map for businesses looking to automate and optimise processes using AI in order to attain operational excellence and sustainable growth. © 2025 ieee.

关键词： Decision making

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6th international conference on cyber Security Intelligence and Analytics, CSIA 2024

6th International Conference on Cyber Security Intelligence ...

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6th international conference on cyber Security Intelligence and Analytics, CSIA 2024

ISBN: (纸本)9783031882869

The proceedings contain 56 papers. The special focus in this conference is on cyber Security Intelligence and Analytics. The topics include: Comparative Study of ARIMA Model and Long Short Term Memory Network (LSTM) in Economic Management;the Application and Empirical Study of Causality in the Theory Graph;research on Machining and Simulation Optimization System of Automobile Steering Knuckle Based on Advanced Algorithm;network Partitioning and Demand Characterization for Management of Urban Low Voltage Power Distribution systems;automation and Data Aggregation Algorithm for Low Code Development in Power Grid Mobile Report Generation;big Data Analysis and Smart Grid Security Event Monitoring and Response in the Power Internet of Things;Construction of an Online Autonomous Learning Model Based on Artificial Intelligence ChatGPT;Application of PMS Graphic intelligent Recognition and Analysis Based on Contact Diagram Automatic Generation technology;application of Genetic Algorithm in Reasonableness Evaluation of Environmental Design Space Layout;construction of an intelligent Recommendation Model for Digital Media Content Based on Artificial Intelligence;research on Link Selection and Allocation for IoT Localization systems Based on an Improved Ant Colony Algorithm;cross-Domain Sharing and Privacy Protection Method of Fused Multi-source Data in Visual Internet of Things;construction of Cold Chain Logistics and Distribution Site Selection System Based on Multi-objective Optimization Model;hole Detection Algorithm Based on Channel Fusion Siamese Network;intelligent Database Triggers Enable Advanced Analysis of Data Recorded in Audit Logs;improvement of Principal Component Analysis Algorithm and Its Simulation Experiment;OLAP technology Financial Statistics Information Platform Based on Big Data Analysis.

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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-...

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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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An intelligent Security-Enabled Home automation System using Internet of Things Environment 3

An Intelligent Security-Enabled Home Automation System using...

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3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Karthikeyan, P. Dinesh, M. Akila, A. Veeramuthu, A. Giri, Jayant Al Sherideh, Ala'a Prathyusha Engineering College Department of Eee Tiruvallur Chennai India Saveetha School of Engineering Department of Vlsi Microelectronics Chennai India R.M.K. College of Engineering and Technology Department of Artificial Intelligence and Data Science Tamil Nadu Puduvoyal India Sathyabama Institute of Science and Technology Department of Information Technology Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India Zarqa University College of Information Technology Department of Cyber Security Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331509675

IoT technologies to bolster residential security, safety, and sustainability. By leveraging facial recognition for door access, enhancing fire safety monitoring, and integrating energy harvesting, it aims to create more resilient and eco-friendly residential environments. These innovations offer comprehensive solutions to address evolving challenges and optimize residential living. The proposed research introduces an innovative smart home automation system leveraging IoT technologies and Google Assistant for the voice-controlled operation of household devices. It integrates a comprehensive array of sensors for safety and security monitoring, facilitating real-time alerts for fire outbreaks, unauthorized entry, and environmental anomalies. MQTT enables smooth remote access and management of devices. The research evaluates IoT routing protocols, highlighting ISHAS-IoTs's benefits in efficiency, delivery ratio, and delay. These findings underscore the protocol's potential to enhance IoT network performance and efficiency, contributing to the design of more robust and reliable smart home systems. © 2025 ieee.

关键词： Smart homes

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A Two-Stage Extended Kalman Filter-Based Approach Against FDI cyber-Attack in intelligent and Connected Vehicles

A Two-Stage Extended Kalman Filter-Based Approach Against FD...

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11th international conference on Vehicle technology and intelligent Transport systems, VEHITS 2025

作者： Sun, Bin Yang, Shichun Wang, Yu Lu, Jiayi Cao, Yaoguang School of Transportation Science and Engineering Beihang University Beijing China State Key Lab of Intelligent Transportation System Beihang University Beijing China

ISBN: (纸本)9789897587450

With the widespread integration of artificial intelligence and telecommunication technologies in vehicles, the challenge of cybersecurity in intelligent and Connected Vehicles (ICVs) has gained significant attention. A typical and high-risk cyber-attack technique involves False Data Injection (FDI) into sensors through the network, resulting in deviations in subsequent planning and control algorithm outcomes. Existing approaches suffer from limited robustness, being suitable only for simple models or requiring extensive data for the training model, which limits their practicality. Therefore, this paper proposes a method based on a Two-stage Extended Kalman Filter (TSEKF), which not only detects cyber-attacks but also restores the vehicle’s true motion state, thereby enhancing the robustness of vehicle ego state perception. The experimental results demonstrate that the proposed method exhibits strong performance across various motion scenarios, offering an effective solution for the safe operation of ICVs. Copyright © 2025 by SCITEPRESS - Science and technology Publications, Lda.

关键词： cyber attacks

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Systematic Study on AI-Enabled Defense Against DDoS Attacks in IoT 1

Systematic Study on AI-Enabled Defense Against DDoS Attacks ...

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

作者： Vadlamudi, Sandyarani Viswa Bharathy, A.M. Gitam School of Technology Department of Cse Bangalore India

ISBN: (纸本)9798331529246

People and machines are learning from the internet of things such as sensors, actuators, services and other things. Many facets of contemporary life have changed as a result of the Internet of Things (IoT), including home automation, industrial control systems, healthcare, and transportation. As the Internet of things applications are increasing attacks posing on these applications also increasing. One of the most dangerous threats when it comes to Internet of things is Distributed Denial of Service attack in which the attacks make services unavailable for the users. While the individuals responsible for DoS attacks, specifically DDoS attacks, do not intend to steal data, these attacks still pose a significant risk to networks. DDoS attacks aim to exhaust the resources of the targeted system, rendering it unable to provide its intended services. As a result, the system becomes dysfunctional. This study discussed about many machine learning and deep leaning algorithms for the DDoS attacks for several datasets that included IoT Network data. © 2025 ieee.

关键词： Network security

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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 ...

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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...

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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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intelligent Power Consumption Measurement and control System Based on C/S and Euclidean Distance 5

Intelligent Power Consumption Measurement and Control System...

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5th ieee international conference on Power, Electronics and Computer Applications, ICPECA 2025

作者： Xiao, Yuanzheng Hong, Huawei Chen, Feifei Qian, Xiaorui Xu, Ming Feng, Huang Fuzhou Fujian350013 China State Grid Info-telecom Great Power Science and Technology Co. Ltd. Fuzhou350003 China

ISBN: (纸本)9798331533694

With the rapid development of smart grids, intelligent power measurement and control systems have become a key technology for achieving efficient energy management and energy conservation and emission reduction. Traditional measurement and control systems often have problems such as low data processing efficiency and poor real-time performance, which makes it difficult to meet the needs of modern power grids for precise control and intelligent management. In response to the above problems, this paper aims to explore an intelligent electricity measurement and control system based on C/S (Client/Server) architecture and Euclidean distance algorithm to improve data processing efficiency and system real-time performance. This paper elaborates on the application of C/S algorithm and Euclidean distance in intelligent power measurement and control system, including system architecture design, data processing flow and algorithm implementation. Then, through system performance testing and experimental results analysis, the effectiveness and reliability of the proposed system are verified. Finally, the research content, experimental results and shortcomings of this paper are summarized, and directions for future improvement are proposed. The experimental results show that the intelligent electricity measurement and control system based on C/S and Euclidean distance can significantly improve data processing efficiency and system real-time performance. The highest speed of data processing in the system is 200, reducing energy consumption and operating costs, providing strong support for the intelligent management of smart grids. © 2025 ieee.

关键词： C/S Algorithm Euclidean Distance intelligent Power Consumption Measurement and control System

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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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