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3rd International conference on Intelligent Computing, Instrumentation and control Technologies, ICICICT 2022

作者： Sagar, Maloth Vanmathi, C. Vellore Institute of Technology School of Information Technology and Engineering Tamilnadu Vellore India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Cyber Physical systems (CPS), which contain devices to aid with physical infrastructure activities, comprise sensors, actuators, control units, and physical objects. CPS sends messages to physical devices to carry out computational operations. CPS mainly deals with the interplay among cyber and physical environments. The real-time network data acquired and collected in physical space is stored there, and the connection becomes sophisticated. CPS incorporates cyber and physical technologies at all phases. Cyber Physical systems are a crucial component of internet of Things (IoT) technology. The CPS is a traditional concept that brings together the physical and digital worlds inhabit. Nevertheless, CPS has several difficulties that are likely to jeopardise our lives immediately, while the CPS's numerous levels are all tied to an immediate threat, therefore necessitating a look at CPS security. Due to the inclusion of IoT devices in a wide variety of applications, the security and privacy of users are key considerations. The rising level of cyber threats has left current security and privacy procedures insufficient. As a result, hackers can treat every person on the internet as a product. Deep Learning (DL) methods are therefore utilised to provide accurate outputs from big complex databases where the outputs generated can be used to forecast and discover vulnerabilities in IoT systems that handles medical data. Cyber-physical systems need anomaly detection to be secure. However, the rising sophistication of CPSs and more complex attacks means that typical anomaly detection approaches are unsuitable for addressing these difficulties since they are simply overwhelmed by the volume of data and the necessity for domain-specific knowledge. The various attacks like DoS, DDoS need to be avoided that impact the network performance. In this paper, an effective network Cluster Reliability Model with enhanced security and privacy levels for the data in IoT for Anomaly Detection

关键词： internet of things

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Comparative analysis of RPL performance under different wireless network topologies 3

Comparative analysis of RPL performance under different wire...

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3rd International conference on Power, Energy, control and Transmission systems, ICPECTS 2022

作者： Shabana, Parveen M. Bhuvaneswari, P.T.V. Sri Sairam Engineering College Dept. of Electronics and Communication Engineering Chennai India Madras Institute of Technology Dept. of ElectronicsEngineering Chennai India

ISBN: (纸本)9781665462754

IPV6 Routing Protocol for Low Power Lossy networks (RPL) is one of the routing protocols that can be used for internet of Things networks based applications. In this study, the performance of RPL with linear and ellipse topologies is examined. Two scenarios are considered case 1, all nodes are assumed to be static, while case 2, some nodes are made dynamic. In terms of Packet Delivery Ratio (PDR), control overhead, convergence time and energy usage, RPL's performance on these topologies is assessed. The analysis is performed in a COOJA simulator environment. The topology with improved energy efficiency and reliability is chosen. The outcomes demonstrate that elliptical topology performs admirably in both situations. © 2022 IEEE.

关键词： Topology

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Enhanced network Anomaly Detection Using Autoencoders: A Deep Learning Approach for Proactive Cybersecurity

Enhanced Network Anomaly Detection Using Autoencoders: A Dee...

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Intelligent Cyber Physical systems and internet of Things (ICoICI), International conference on

作者： Judy Simon Nellore Kapileswar Rajamanickam Vani Narala Madhukara Reddy Dudekula Moulali Anki Reddy Narayana Reddy Dept. of Electronics and Communication Engineering SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

Anomaly detection in network traffic is core part of modern network management. It plays vital role in finding the security threats and performance issues. Finding the threats using the machine learning (ML) method provides an efficient solution to dynamically lean normal network behaviour and detect deviation indicative of anomalies. This study proposed a Support vector machine with Autoencoder for detecting the anomalies in network traffic. Implementing ML methods for threat identification improves network security by enabling rapid response to potential threats and issues. Hence this method also reducing downtime and improving overall network reliability. This study indicates the benefits of ML driven anomaly detection, highlighting its capability to provide a proactive security posture, rapid identification and resolution of anomalies ensuring an efficient network infrastructure.

关键词： Support vector machines performance evaluation Deep learning Telecommunication traffic network security Traffic control Vectors Reliability internet of Things Anomaly detection

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Not a Trade-Off: On the Wi-Fi Energy Efficiency of Effective internet Congestion control 17

Not a Trade-Off: On the Wi-Fi Energy Efficiency of Effective...

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17th conference on Wireless On-Demand network systems and Services (WONS)

作者： Welzl, Michael Univ Oslo Oslo Norway

ISBN: (纸本)9783903176461

It is common to regard performance and energy efficiency as a trade-off, and this is a characteristic of many proposed solutions for energy efficiency. This is disadvantageous, as it is tempting for administrators or end users to disable energy conserving mechanisms when they have a performance cost. In contrast, this article makes the point that improvements in internet congestion control can be inherently energy-efficient: for example, minimizing the Flow Completion Time (FCT) of data transfers, one of the most common goals in congestion control, can significantly reduce the energy usage of a Wi-Fi receiver.

关键词： Wi-Fi Energy Efficiency Congestion control TCP

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A Mobile internet Multi-level Two-way Identity Authentication Scheme Based on Zero Trust 23

A Mobile Internet Multi-level Two-way Identity Authenticatio...

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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： Chen, Lu Sun, Yuwei Sun, Zhixin Nanjing China

ISBN: (纸本)9781665494571

With the development of the smart city, power mobile internet services have been widely used. The gradual opening of the power mobile service system has made the network boundary increasingly blurred. The interaction of various services puts forward higher requirements for the flexibility, accuracy, and moderation of user identity authentication. Existing authentication methods assume that the users, equipment, and processes of the intranet are credible, and lack corresponding technical means in terms of risk prevention and control. Combining dynamic and static authentication methods, this paper proposes a multi-level intelligent identity authentication scheme under the zero-trust architecture. This solution realizes the two-way authentication between the user and the server in the mobile internet service. Compared with the existing scheme, this scheme conducts continuous trust assessment and risk prevention for users based on static authentication and constructs a two-way authentication protocol between the user and the server based on attribute encryption. Security analysis shows that this solution realizes the security authentication between the subject to be authenticated and the server, guarantees the confidentiality of the authentication ciphertext, and can resist forgery attacks. © 2021 IEEE.

关键词： Protocols Smart cities Web and internet services Authentication Process control Resists Forgery

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Opportunistic Wireless control Over State-Dependent Fading Channels 61

Opportunistic Wireless Control Over State-Dependent Fading C...

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IEEE 61st conference on Decision and control (CDC)

作者： Wang, Shuling Li, Peizhe Zhu, Shanying Chen, Cailian Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Minist Educ China Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China Shanghai Engn Res Ctr Intelligent Control & Manag Shanghai 200240 Peoples R China

ISBN: (纸本)9781665467612

The heterogeneous system consisting of the wireless control system (WCS) and mobile agent system (MAS) is ubiquitous in Industrial internet of Things (iioT) systems. Within this system, the positions of mobile agents may lead to shadow fading on the wireless channel that the WCS is controlled over and can significantly compromise the WCS's performance. This paper focuses on the controller design for the MAS to ensure the performance of WCS in the presence of WCS and MAS coupling. Firstly, the constrained finite field network (FFN) with profile-dependent switching topology is adopted to proceed the operational control for the MAS. By virtue of the algebraic state space representation (ASSR) method, an equivalent form is obtained for the WCS and MAS coupling. A necessary and sufficient condition in terms of constrained set stabilization is then established to ensure the Lyapunov-like performance with expected decay rate. Finally, a graphical method together with the breath-first searching is provided to design state feedback controllers for the MAS. With this method, it is easy to check the constrained set stabilization of MAS and to ensure the performance requirements of WCS in the presence of WCS and MAS coupling. The study of an illustrative example shows the effectiveness of the proposed method.

关键词： controllers

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Imaginations Generate Images for Multi-modal Machine Translation 1

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13th International conference on Computer Engineering and networks (CENet)

作者： Yang, Xiaona Sun, Wenli Wei, Wei Li, Yinlin Shi, Xiayang Zhengzhou Univ Light Ind Zhengzhou Peoples R China Wuhan Second Ship Design & Res Inst Wuhan Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (数字)9789819992430

ISBN: (纸本)9789819992454;9789819992430;9789819992423

Multi-modal machine translation (MMT) aims at exploring better translation systems by integrating the visual annotation which presents the content described in the bilingual parallel sentence pair into the conventional only-text neural machine translation (NMT). However, existing methods heavily rely on the manual annotated images data set. The cost of manual image annotation is relatively high at this stage. In this paper, we propose the generative imagination network with transformer to automatically generate visual annotations semantic-equivalent with source and target sentences. The proposed model receives the inputs of source-target bilingual sentences and generates visual annotations for MMT. Experiments analysis demonstrate that our model can generate high-quality annotated images and prompt the performance of MMT. Additionally, we use our model to generate annotated images for a famous English-German IWSLT-2015, the experimental results show the improvement for MMT.

关键词： Multi-modal machine translation Visual annotation

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An intelligent routing algorithm based on energy-efficient progressive-recursive clusters for wireless sensor networks 6

An intelligent routing algorithm based on energy-efficient p...

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6th International conference on Advanced Communication Technologies and networking, CommNet 2023

作者： Hilmani, Adil Koundi, Mohammed Aouad, Siham Sabri, Yassine Maizate, Abderrahim Ayoub, Fouad Mohamed v University City of Rabat Superior School of Technologies of Sale Morocco Ibn Tofail University Ensa Kenitra Morocco Mohamed v University Smart Systems Laboratory ENSIAS Rabat Morocco Ibn Zohr University Laboratory of Metrology and Information Processing - Lmti FacuIty of Sciences Agadir Morocco University Hassan Ii RITM-ESTC/CED-ENSEM Casablanca Morocco Eratic Team LaREAMI Lab CRMEF-K Kenitra Morocco

ISBN: (纸本)9798350329391

The implementation of wireless sensor networks (WSNs) is central to the operation of smart parking in modern cities. These networks are responsible for monitoring and tracking the occupancy of each parking space in real-time. The WSNs consist of sensor nodes that detect, process, and transmit data to the base station at regular intervals. However, power and communication limitations in these nodes can impact the performance and quality of the network. Early energy depletion, for instance, can shorten the network's lifespan and necessitate the replacement of sensor nodes. To address these challenges, this paper proposes a WSN routing protocol that employs clustering to build clusters and facilitate multi-hop communication to the base station. This approach offers a robust and efficient algorithm that reduces power dissipation and extends the lifespan of both sensor nodes and the WSN network. Simulation results indicate that this proposed protocol is more energy-efficient, reliable in data transmission, and longer-lasting compared to other protocols commonly used in smart parking management. © 2023 IEEE.

关键词： internet of things

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Research on Vehicle Distance Estimation Model based on Deep Learning 41

Research on Vehicle Distance Estimation Model based on Deep ...

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第41届中国控制会议

作者： Cheng Ding Jianmin Bao Guanyu Mi Xiao Kuai YiNing Kang School of Internet of Things Nanjing University of Posts and Telecommunications

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

With the continuous development of artificial intelligence technology,intelligent vehicle technology in driverless scenarios has become an important direction for a new round of technological *** an important part of the driverless car,the vehicle distance estimation module is of great significance for improving the intelligent car in the driverless environment,and the reliability and safety of the system are of great ***,in the field of unmanned driving,traditional vehicle distance estimation algorithms have the problems of poor real-time performance and insufficient accuracy,and cannot achieve end-to-end distance estimation *** at the above problems,this paper proposes a deep learning-based vehicle distance estimation *** distance estimation module is added to the traditional mainstream deep learning network so that the distance estimation task and the original classification and detection task can achieve feature fusion,and multi-task joint learning is performed to realize the end-to-end distance estimation *** simulation results show that the model proposed in this paper can effectively make up for the lack of realtime performance of traditional distance estimation methods based on machine learning,and achieve higher distance estimation accuracy and better real-time performance.

关键词： Vehicle Detection Attention Mechanism Vehicle Distance Estimation Deep Learning

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Attack-specific feature analysis framework for NetFlow IoT datasets

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Computers and Security 2025年 157卷

作者： Choi, Dayoung Rheey, Joohong Park, Hyunggon Department of Electronic and Electrical Engineering Ewha Womans University Seoul03760 Korea Republic of

With the deployment of a vast number of internet of Things devices across diverse applications, new security vulnerabilities have emerged. Since internet of Things devices often have significantly limited resources, the intrusion detection system for internet of Things networks should be efficiently designed with minimum power consumption. As feature selection is a widely used method to reduce the complexity of network traffic data by eliminating unnecessary or redundant features, a framework for attack-specific feature analysis based on feature selection is proposed to design intrusion detection systems in internet of Things networks efficiently. The proposed framework identifies the important features relevant to specific attack types, especially in class-imbalanced internet of Things datasets, whereas the traditional feature analysis framework for the intrusion detection system applies feature selection approaches to entire datasets regardless of attack types. Furthermore, attack-specific intrusion detection systems are built using only a few important features selected by feature analysis. A comprehensive analysis using NetFlow internet of Things datasets, NF-BoT-IoT-v2 and NF-ToN-IoT-v2, is conducted in the experiments with six filter-based feature selection algorithms and two unsupervised learning-based intrusion detection systems. The experiment results show the performance enhancement of attack-specific intrusion detection systems, thus confirming the effectiveness of the proposed framework. The proposed framework improves detection accuracy for all attack types by an average of 38.36% when using Isolation Forest and an average of 2.84% when using autoencoder. © 2025 Elsevier Ltd

关键词： network intrusion

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