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检索条件"任意字段=Conference on Internet Quality and Performance and Control of Network Systems"

共 10552 条记录，以下是591-600 订阅

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Detection of network Attacks on Application Servers Using Deep Learning in IoT Environments 5

Detection of Network Attacks on Application Servers Using De...

引用

5th International conference on Advancements in Computing, ICAC 2023

作者： Meegammana, Niranjan W. Fernando, Harinda Shilpa Sayura Foundation Cyber Security Research Lab Kandy Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9798350358131

internet of Things (IoT) comprises interconnected smart devices that collect data, control systems, are increasingly used in critical infrastructure, and raise security concerns due to their inherent vulnerabilities, where conventional security measures struggle to defend IoT networks. This research explores the use of Deep Learning for detecting network attacks on IoT application servers, addressing vital security concerns. The study, employing the Artificial Neural network (ANN) DL model, achieved an impressive 93% accuracy and precision Score of 0.99, highlighting its robustness in identifying various network attack types on IoT application servers. Furthermore, it demonstrated strong performance in terms of precision, recall, and F1 scores, around 0.95, 0.92, and 0.93, respectively, showcasing the ANN model's ability to make precise predictions while minimizing false positives. These findings indicate that DL, especially the ANN model, significantly strengthens IoT security by safeguarding application servers from diverse security threats. Future work will focus on countering emerging zero-day attacks and further integrating the ANN model with application firewalls to enhance IoT security. © 2023 IEEE.

关键词： internet of things

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Deep Reinforcement Learning for Power control in Next-Generation WiFi network systems

Deep Reinforcement Learning for Power Control in Next-Genera...

引用

IEEE Military Communications conference (MILCOM)

作者： El Jamous, Ziad Davaslioglu, Kemal Sagduyu, Yalin E. BlueHalo Rockville MD 20855 USA Univ Tech Serv Inc Greenbelt MD USA Virginia Tech Natl Secur Inst Arlington VA USA

ISBN: (纸本)9781665485340

This paper presents a deep reinforcement learning (DRL) solution for power control in wireless communications, describes its embedded implementation with WiFi transceivers for a WiFi network system, and evaluates the performance with high-fidelity emulation tests. In a multi-hop wireless network, each mobile node measures its link quality and signal strength, and controls its transmit power. As a model-free solution, reinforcement learning allows nodes to adapt their actions by observing the states and maximize their cumulative rewards over time. For each node, the state consists of transmit power, link quality and signal strength;the action adjusts the transmit power;and the reward combines energy efficiency (throughput normalized by energy consumption) and penalty of changing the transmit power. As the state space is large, Q-learning is hard to implement on embedded platforms with limited memory and processing power. By approximating the Q-values with a deep Q-network (DQN), DRL is implemented for the embedded platform of each node combining an ARM processor and a WiFi transceiver for 802.11n. controllable and repeatable emulation tests are performed by inducing realistic channel effects on RF signals. performance comparison with benchmark schemes of fixed and myopic power allocations shows that power control with DRL provides major improvements to energy efficiency and throughput in WiFi network systems.

关键词： Power control WiFi deep reinforcement learning energy efficiency throughput embedded implementation edge computing emulation tests

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The Computer Vision System for a Crewless Boat

The Computer Vision System for a Crewless Boat

引用

2024 International conference on Industrial Engineering, Applications and Manufacturing, ICIEAM 2024

作者： Kramar, Vadim A. Kramar, Oleg A. Alchakov, Vasiliy V. Sevastopol State University Department Of Informatics And Control In Technical Systems Sevastopol Russia

ISBN: (纸本)9798350395013

The article considers a computer vision system for a crewless ship. The existing approaches to the construction of such systems are described, and the strengths and weaknesses of different methods are analyzed. The hardware part of the system is considered. The functional scheme of the system and technical characteristics of the equipment are given. An approach to solving the problem of detecting a given class of objects based on the YOLOv5 algorithm is described. The accuracy of the obtained model is confirmed with the help of precision-recall curve. The paper also presents an algorithm for calculating the heading angle for the dynamic positioning of an uncrewed vessel based on data from the vision system. The advantage of the proposed approach is its fast performance, which allows the use of onboard computers in real-time. The highly effective strategy allows the onboard system's image to be broadcast over a local network without losing image quality. The performance of the computer vision system and the algorithm for detecting a given class of objects is verified using long-term hard-loaded testing. The test results showed good accuracy and stability of the developed system. © 2024 IEEE.

关键词： Computer vision

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performance Evaluation of Resource Allocation in Software Defined network

Performance Evaluation of Resource Allocation in Software De...

引用

2023 IEEE conference on Computer Applications, ICCA 2023

作者： Kyaw, Aung Than Zaw, Hnin Thiri Aung, Thandar Maw, Aung Htein Mon, Myat Thida Research Lab Faculty of Computer Systems and Technologies Yangon Myanmar

ISBN: (纸本)9781665435994

In Software-Defined networking (SDN) area, the implementation of quality of Service (QoS) is still ongoing. SDN provides flexibility and benefits to the network by separation of control and data planes with its centralized software-based ar-chitecture and programmable functionalities. QoS control in SDN generally refers to network mechanisms used to guarantee a particular service for the resources in the network. One of the parameters in the networked resources includes the bandwidth. As a lot of traditional service providers use hard QoS controls to guarantee bandwidth to their increasing number of users, excess idle bandwidth is wasted when all the users are not using the reserved resources fully all the time. In this paper, fairly distrib-uting the excess bandwidth among current users using Dynamic Bandwidth Allocation (DBA) algorithm to maximize link capacity utilization is presented. For evaluation, SDN traffic flows based on Transmission control Protocol (TCP) and User Data-gram Protocol (UDP) generated from two hosts (Source) to the server (Destination) are analysed by using the Mininet Emula-tor, OpenFlow enabled CPqD switches, Python-based Ryu con-troller and the network Speed Testing and performance Meas-urement Tool (iPerf). In the performance evaluation of dynamic bandwidth allocation for both traffic, the controller allocated bandwidth of the UDP traffic takes place 80 100 % of theoret-ical value calculated by the algorithm and the allocation band-width of the controller for TCP is 30 50 % of the algorithm's expected value. © 2023 IEEE.

关键词： Software defined networking

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SMFN: A small target detection method in nylon yarn package images based on neural network

SMFN: A small target detection method in nylon yarn package ...

引用

2023 International conference on Artificial Intelligence, systems and network Security, AISNS 2023

作者： Wu, Jinda Li, Qiang Institute of Intelligent and Software Technology Hangzhou Dianzi University Hangzhou310018 China

ISBN: (纸本)9798400716966

Nylon, a vital synthetic fiber, plays a crucial role in various industries. Detecting small-sized defects on nylon yarn packages presents a formidable challenge in quality control. In response to this challenge, we propose an innovative approach based on YOLOv5, introducing the Spatially Sensitive Multi-Mode Feature Fusion Neural network (SMFN). The key innovation in SMFN is the development of a spatially sensitive attention module (SSA). This module enhances feature extraction, capturing a wealth of spatial information while improving the detection accuracy for smaller targets. Furthermore, we introduce a multi-mode feature fusion module (MFF), replacing upsampling module. This modification enables the neural network to autonomously learn feature fusion, leading to superior performance. On a dedicated dataset of nylon yarn packages, The SMFN model achieves a remarkable mAP@0.5 score of 66.9%. This represents a substantial 5.7% improvement over the baseline YOLOv5 model, while maintaining a rapid detection speed of 59.42 frames per second. In summary, SMFN empowers the swift and high-precision small target detection of defects in nylon yarn packages, promising enhanced quality control in manufacturing processes. © 2023 ACM.

关键词： quality control

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Anomaly Detection in IoT Sensor networks using Machine Learning

Anomaly Detection in IoT Sensor Networks using Machine Learn...

引用

2022 International conference on Computing, Communication, Security and Intelligent systems, IC3SIS 2022

作者： Mithran, Kavya Gopi, C. Government Engineering College Department of Electronics and Communication Engineering Thrissur India

ISBN: (数字)9781665468831

ISBN: (纸本)9781665468831

Anomaly Detection is a promising new approach for quality control in wireless networks and telecommunication networks. New modern network architecture enables intensive computing and communication at the edge of the network. It can thus support large scale IoT applications. The proper and timely detection of attacks in the internet of Things (IoT) infrastructure is very important. With the rise in IoT technologies in various application areas these days, the attacks and threats are also on the verge of exploiting this. Denial of Service, Data Type Probing, Malicious control, Malicious Operation, Scanning, Spying and Wrong Setup are the different attacks and anomalies which can cause a communication system failure. In the past decades an enormous rise in the internet applications was seen, which has led to concreting the need of security of the information networks by many folds. As the network operations continuously changes in different time frames, an anomaly detection system should be capable of adapting dynamically to the situations. The objective of this paper is to identify various attacks on IoT sensor networks and the development of a generalized anomaly detection model using machine learning techniques to check if an IoT sensor network is anomalous or not. Kaggle and NSL-KDD datasets are used for this study. Model is trained based on the Levenberg - Marquardt optimization using TRAINLM training function and LEARNGDM adaptation learning function. High performance was exhibited by the model. Menu based environment is also created in Jupyter in this paper. © 2022 IEEE.

关键词： internet of things

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TaDA: Task Decoupling Architecture for the Battery-less internet of Things 24

TaDA: Task Decoupling Architecture for the Battery-less Inte...

引用

22nd ACM conference on Embedded networked Sensor systems, SenSys 2024

作者： Song, Weining Kaxiras, Stefanos Voigt, Thiemo Yao, Yuan Mottola, Luca Uppsala University Uppsala Sweden Uppsala University RISE Sweden Politecnico di Milano Milan Italy RISE Uppsala Universit Sweden

ISBN: (纸本)9798400706974

We present TaDA, a system architecture enabling efficient execution of internet of Things (IoT) applications across multiple computing units, powered by ambient energy harvesting. Low-power microcontroller units (MCUs) are increasingly specialized;for example, custom designs feature hardware acceleration of neural network inference, next to designs providing energy-efficient input/output. As application requirements are growingly diverse, we argue that no single MCU can efficiently fulfill them. TaDA allows programmers to assign the execution of different slices of the application logic to the most efficient MCU for the job. We achieve this by decoupling task executions in time and space, using a special-purpose hardware interconnect we design, while providing persistent storage to cross periods of energy unavailability. We compare our prototype performance against the single most efficient computing unit for a given workload. We show that our prototype saves up to 96.7% energy per application round. Given the same energy budget, this yields up to a 68.7x throughput improvement. © 2024 Copyright is held by the owner/author(s).

关键词： Budget control

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Study on Active Defense of Honeypot-Based Industrial control network 23

Study on Active Defense of Honeypot-Based Industrial Control...

引用

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： Wei, Xunsi Yang, Dequan Beijing Institute of Technology School of Computer Beijing100081 China Network Information Technology Center Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9781665494571

With the rapid development of information, network security and information have played an increasingly important role in national production. As a result, the environment and application of industrial control networks are under more and more network security threats. Furthermore, most applications of industrial control networks belong to critical infrastructure, so to guarantee the security of industrial control networks has become more significant, and the defense of it is one of the essential issues in need of being solved. This paper focuses on studying the active protection for the security of industrial control networks and discusses the role of honeypot in the network security for the defense strategy of the industrial control network. Firstly, this paper introduces the theoretical knowledge of honeypots. Secondly, it analyzes the existing problems and development prospects of the honeypot. And finally, under the background of the industrial control network, it designs the SSH honeypot based on Transmission control Protocol/internet Protocol (TCP/TP), which is divided into two modules, including SSH information collection and alarm. This system can deploy based on the Docker of Linux. Through simulation practice, it proves that honeypot can effectively capture the attackers' attack and make an early warning to improve network security. © 2021 IEEE.

关键词： Industrial control Linux TCPIP Production network security Critical infrastructure

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Blockchain-based Authentication Scheme for Vehicle network Nodes 3

Blockchain-based Authentication Scheme for Vehicle Network N...

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3rd International conference on Blockchain Technology and Information Security, ICBCTIS 2023

作者： Di, Xiaoqiang Sun, Yitong Lu, Junchu Dai, Weiyi Qi, Hui Changchun University of Science and Technology Jilin Province Changchun China

ISBN: (纸本)9798350340402

internet of Vehicles (IoV) has developed into a useful service platform between vehicles and other vehicles, vehicles and roadways, and vehicles and pedestrians as a result of advancements in communication technology. However, along with the intelligent and networked IoV, IoV security issues are increasingly emerging, which often lead to extremely serious consequences. For example, when a vehicle is remotely attacked and maliciously controlled, it can lead to user privacy leakage. Attackers can also control the driving of vehicles through attacks, and there may even be a large number of connected cars being controlled in bulk, ultimately causing major social security incidents. In the internet of Vehicles, authentication technology is one of the main solutions to address the security issues of the internet of Vehicles. Many researchers have conducted some research on the authentication technology of vehicular network, but the traditional authentication scheme under the centralized system has problems such as frequent single-point failures and high overhead. When blockchain technology is properly implemented to the vehicle network environment, its security outperforms that of centralized systems due to features like decentralization, tamper-proof, security, and traceability. Because of this, we construct an optimization technique based on the Practical Byzantine Fault Tolerance (PBFT) algorithm in this work and present an efficient IoV decentralized authentication scheme based on the consensus algorithm of blockchain technology. After performance analysis, it is proved that our scheme has better performance and higher security. © 2023 IEEE.

关键词： Blockchain

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DNN-based Long Prediction Horizon Finite control Set MPC with Switching Effort Penalization 50

DNN-based Long Prediction Horizon Finite Control Set MPC wit...

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50th Annual conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Acosta, Ignacio A. Cedeño, Angel L. Carvajal, Gonzalo Agüero, Juan C. Silva, César A. Departamento de Electrónica Universidad Técnica Federico Santa María Valparaíso Chile

ISBN: (纸本)9781665464543

This article explores the design and analysis of deep neural network-based approximators for constrained finite control set model predictive control (MPC) with long prediction horizons in power electronics applications. The study focuses on the impact of the prediction horizon on the penalization of switching effort and examines how these approximators inherit operational characteristics from traditional finite control set MPC controllers. The integration of deep neural networks aims to reduce the computational demand typically associated with finite control set MPC, while ensuring high fidelity in approximating the optimal control policy. The performance is evaluated through metrics such as total harmonic distortion, switching effort, voltage quality using fast Fourier transform analysis, and execution time. Simulation scenarios include both linear and nonlinear loads in uninterruptible power supply systems, highlighting the effectiveness and adaptability of the proposed approach in modern power electronics applications. © 2024 IEEE.

关键词： Uninterruptible power systems

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