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Software-Defined Intrusion Detection System for DDoS Attacks in IoT Edge Networks

Software-Defined Intrusion Detection System for DDoS Attacks...

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IEEE international symposium on Dependable, Autonomic and Secure computing (DASC)

作者： Burcu Bolat-Akça Elif Bozkaya Department of Computer Engineering National Defence University Istanbul Turkey Department of Computer Engineering National Defence University Turkish Naval Academy Istanbul Turkey

The rapid advance of the Internet of Things (IoT) has led to the emergence of various applications, such as smart healthcare, smart transportation, smart grid, and more. However, IoT consists of resource-constrained smart devices with limited storage and processing capabilities. Against this backdrop, the edge computing paradigm is considered as one of the promising technologies to improve system performance; here integrating edge computing with the IoT solves the computational and processing problems of IoT devices. Despite the wide usage of this integration, new challenges have emerged in security provisioning since traditional security schemes for the Internet are difficult to apply for different IoT-based applications. Thus, in this paper, software-defined networking (SDN) technology is adopted to address this issue and we propose a novel software-defined intrusion detection framework against distributed Denial of Service (DDoS) attacks in IoT edge networks. In the proposed framework, first, we introduce a three-layer system architecture through an efficient collaboration between the SDN controller and edge servers. Second, we define different types of DDoS attacks and analyze their characteristics. Third, we propose an intrusion detection algorithm and provide a comparative analysis to enhance accuracy performance. By doing this, we also define the best parameters of different classifiers. Experimental results show that our proposed framework can achieve superior accuracy performance to identify DDoS attacks and enhance the security of IoT edge networks.

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Improve the Performance of LSM-Tree based on Key-Value via Multithreading

Improve the Performance of LSM-Tree based on Key-Value via M...

引用

international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： Yuan Gao Ping Xie Wendi Hua Meng Lv Jiating Lu College of Computer of Qinghai Normal University Xining P. R. China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining P. R. China Key Laboratory of Internet of Things of Qinghai Province Xining P. R. China Academy of Plateau Science and Sustainability Xining P. R. China School of Physical and Electrical Engineering Qinghai Normal University Xining P. R. China

ISBN: (纸本)9781665428903

In the era of big data, key-value storage systems based on Log-Structure Merge tree (LSM-tree) are widely used in numerous industries. LSM-Tree is divided into two parts, one part is in the memory and the other part is on the hard disk. When writing data, first write to the memory, when the Immutable Memtable in the memory is full of data, then write to the disk to output a Sorted String Table (SSTable) file. However, the use of writing data from memory to disk is single-threaded. When the Immutable Memtable writes data to the disk, it will block other threads. This article uses multiple Immutable Memtables and multi-threaded writing to solve the single-thread blocking problem. By running the benchmarks of LevelDB to analyze the experiment, the experimental results indicate a higher improvement.

关键词： Industries Multithreading Instruction sets Writing Big Data Benchmark testing Hard disks

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The Electricity Management for Domestic applications Through Hybrid Grid

The Electricity Management for Domestic Applications Through...

引用

international Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Sanjay Kumar Bharti Kalra Kannagi A Department of Mechanical Engineering Noida Institute Of Engineering and Technology Greater Noida Uttar Pradesh India Department of Computer Science and Engineering Tula's Institute Dehradun India Department of Computer Jain (Deemed to be University) Bangalore India

In order to effectively manage power demand, optimize energy usage, and integrate distributed renewable energy production, home energy management systems, or HEMSs, are essential. The primary aim is to optimize energy efficiency while maintaining customer comfort. Numerous factors, including as energy prices, meteorological conditions, load the profiles, and customer comfort levels, influence how HEMSs function. They greatly reduce the amount of power used in both personal and business smart networks as they become more and more common. This study performs a comprehensive analysis of the body of research on HEMS, including key ideas, configurations, and the supporting technologies that support their operation. Giving a thorough review of the status of HEMS innovation and its uses today is the aim. The study also discusses popular communication methods used in demand management applications and analyzes current advancements in HEMS computing. The study that follows provides readers with a comprehensive overview of current and upcoming developments in HEMS technology and solutions. Presenting a polished and original synthesis of the current material while preserving its integrity and excluding any needless elaboration is the aim.

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SW-LCM: A Scalable and Weakly-supervised Land Cover Mapping Method on a New Sunway Supercomputer

SW-LCM: A Scalable and Weakly-supervised Land Cover Mapping ...

引用

international symposium on Parallel and distributed Processing (IPDPS)

作者： Yi Zhao Juepeng Zheng Haohuan Fu Wenzhao Wu Jie Gao Mengxuan Chen Jinxiao Zhang Lixian Zhang Runmin Dong Zhenrong Du Sha Liu Xin Liu Shaoqing Zhang Le Yu Tsinghua University Beijing China Xi’an Institute of Surveying and Mapping Joint Research Center for Next-Generation Smart Mapping Tsinghua University Beijing China National Supercomputing Center in Wuxi Wuxi China National Research Center of Parallel Computer Engineering and Technology Beijing China Qingdao Pilot National Laboratory for Marine Science and Technology Qingdao China

High-resolution land cover mapping (LCM) is an important application for studying and understanding the change of the earth surface. While deep learning (DL) methods demonstrate great potential in analyzing satellite images, they largely depend on massive high-quality labels. This paper proposes SW-LCM, a Scalable and Weakly-supervised two-stage Land Cover Mapping method on a new Sunway Supercomputer. Our method consists of a k-means clustering module as a first stage, and an iterative deep learning module as a second stage. With the k-means module providing a good enough starting point (taking inaccurate results as noisy labels), the deep learning module improves the classification results in an iterative way, without any labelling efforts required for processing large scenarios. To achieve efficiency for country-level land cover mapping, we design a customized data partition scheme and an on-the-fly assembly for k-means. Through careful parallelization and optimization, our k-means module scales to 98,304 computing nodes (over 38 million cores), and provides a sustained performance of 437.56 PFLOPS, in a real LCM task of the entire region of China; the iterative updating part scales to 24,576 nodes, with a performance of 11 PFLOPS. We produce a 10-m resolution land cover map of China, with an accuracy of 83.5% (10-class) or 73.2% (25-class), 7% to 8% higher than best existing products, paving ways for finer land surveys to support sustainability-related applications.

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Coefficient Optimization Based on Genetic Algorithm for Hybrid Control of Semi-active Suspension System

Coefficient Optimization Based on Genetic Algorithm for Hybr...

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international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： Li Wei Zhang Chengcai School of Automotive Engineering Wuhan University of Technology Wuhan China

ISBN: (纸本)9781665428903

Composed of sky-hook control and ground-hook control, hybrid control used for a semi-active suspension system is of great significance for enhancing vehicle comfort, stability, and safety. The former hybrid control is highly difficult in coefficient selection to lead to has a low degree of improvement in suspension performance. In this paper, based on a genetic algorithm, a coefficient optimization method (COGA) is presented for hybrid control of a semi-active suspension system. A genetic algorithm is used to optimize the selection of suspension control system coefficients. Computer simulation of the quarter-vehicle suspension system model is conducted by MATLAB/SIMULINK software. The vehicle comfort and handling characteristics of the suspension systems are predicted under a random road input simulation model. Passive suspension system performance is compared with the semi-active suspension system using hybrid control. The experimental results show that the semi-active suspension hybrid control system with COGA has a minimal root-mean-square value of sprung mass acceleration, suspension dynamic deflection, and dynamic tire load. It can be proved that COGA can effectively coordinate and ameliorate the function of hybrid control.

关键词： Suspensions (mechanical systems) Computational modeling System performance Predictive models Control systems Mathematical models Tires

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Probing the Convergence of Vehicular Edge Metaverse and 6G: Blockchain-enabled Framework

Probing the Convergence of Vehicular Edge Metaverse and 6G: ...

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international symposium on Advanced Networks and Telecommunication Systems (ANTS)

作者： Shree Kumar Rajat Chaudhary Ishan Budhiraja School of Computer Science and Engineering Technology Bennett University Greater Noida India

ISBN: (数字)9798350391725

ISBN: (纸本)9798350391732

The convergence of vehicular networks and the Meta-verse, referred to as the Vehicular Edge Metaverse is set to revolutionize both transportation and in-car experiences. This paradigm shift envisions vehicles not only as modes of transport but also as immersive gateways to the Metaverse, offering passengers interactive and engaging experiences while on the move. The advent of 6G, with its ultra-fast speeds and minimal latency is crucial in enabling the real-time data synchronization and low-latency communication required for a seamless Vehicular Edge Metaverse experience. Additionally, blockchain technology can play a key role in ensuring secure and transparent data sharing and transactions within this ecosystem, fostering trust and enabling new business models. This paper explores the potential of this convergence, discussing the key enabling technologies such as 6G, edge computing, and blockchain, and their role in addressing the challenges of real-time synchronization, resource management, and security. Furthermore, we discuss the potential applications like augmented reality-enhanced driving assistance and immersive in-car entertainment, highlighting the transformative impact of the Vehicular Edge Metaverse on the future of transportation.

关键词： 6G mobile communication Metaverse Transportation Real-time systems Blockchains Security Resource management Synchronization Convergence Testing

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Recognition of power quality disturbance based on artificial bee colony algorithm to optimize kernel extreme learning machine

Recognition of power quality disturbance based on artificial...

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international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： Li-ping Qu Chang-long He Jie Zhang Tai-lu Gao College of Electrical and Information Engineering Beihua University Jilin China

ISBN: (纸本)9781665428903

Aiming at the problem of classification and recognition of power quality disturbance signals, this paper proposes an artificial bee colony algorithm to optimize the classifier model of the kernel extreme learning machine. First, the wavelet transform is used to extract the features of the power quality disturbance signal simulated by Matlab, and then the traditional back-propagation (BP) neural network feedforward neural network is used for classification and recognition. Due to the inherent limitations of the algorithm, the network training calculation is large, the calculation is complicated, and the time is long. Disadvantages, and the proposal of the extreme learning machine just solves this problem, but the accuracy of the classification cannot meet the requirements, and the network needs to be optimized. Therefore, this paper proposes to use the artificial bee colony algorithm to optimize the nuclear extreme learning machine, and classify and recognize the disturbance signal under the optimal parameters of the nuclear extreme learning machine. The simulation results show that after the artificial bee colony algorithm is optimized, the correct rate of classification and recognition has increased by nearly 20%, and the misjudgment rate has dropped to about 3%.

关键词： Training Extreme learning machines Simulation Power quality Artificial bee colony algorithm Feature extraction Classification algorithms

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Optimal Decomposable QAM Constellations for Minimizing Symbol-error Probability

Optimal Decomposable QAM Constellations for Minimizing Symbo...

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IEEE international symposium on Parallel and distributed Processing with applications and IEEE international Conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Brett Wiens Daniel C. Lee School of Engineering Science Simon Fraser University Burnaby Canada

This paper presents signal space constellations that minimize symbol-error probability in an uncoded communications system. We develop a modified projected gradient optimization method to solve for these constellations that scales efficiently, enabling the optimization of very large constellations. The optimized constellation shapes show significant improvements in symbol-error probability over a wide range of channel conditions compared with uniformly-spaced constellations. At high SNR, small deviations from uniform spacing can make substantial improvements in system performance. These results have promising applications to low-complexity systems with real-time data transmission requirements.

关键词： Gradient methods Limiting Shape Quadrature amplitude modulation System performance Symbols Ubiquitous computing

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A Railway Station Scheduling Algorithm Based on Multi-Objective Particle Swarm Optimization Algorithm

A Railway Station Scheduling Algorithm Based on Multi-Object...

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international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： RuiMin Deng YongHua Li JunYa Hu Qi Chen School of Computer Science and Technology Wuhan University of Technology Wuhan China Chongqing Guoyuan Port Co Ltd Chongqing China

ISBN: (纸本)9781665428903

In the railway station scheduling system under the water-rail combined transport mode, how to reasonably allocate equipment resources according to the operation tasks, improve the efficiency of mechanical loading and unloading, and reduce the cost of loading and unloading operations, has become an urgent problem in the process of building a modern port. In this paper, a railway station scheduling algorithm based on multi-objective particle swarm optimization algorithm (RSSAMOPSO) was designed. The multi-objective particle swarm algorithm was used to solve the multi-objective optimization problem of railway station scheduling. Aiming at the problems of decreasing con-vergence speed and the lost of the diversity of the solutions, a compression factor and an adaptive mutation operator are introduced. The improved multi-objective particle swarm algorithm (IM_MOPSO) is combined with the compression factor and adaptive mutation operator to obtain the optimal operation sequence and various equipment task allocation schemes. Experimental results show that the above method has a large practical significance and practical value for improving the efficiency of mechanical loading and unloading in the railway yard and reducing the operating cost of the railway yard.

关键词： Costs Scheduling algorithms Loading Transportation Rail transportation Scheduling Resource management

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Cost-Efficient Cloud-Edge Video Analytics with Hybird IaaS and FaaS Resources

Cost-Efficient Cloud-Edge Video Analytics with Hybird IaaS a...

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international Conference on Parallel and distributed Systems (ICPADS)

作者： Jing Peng Zhi Zhou Kongyange Zhao Huirong Ma Xu Chen School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China

Video analytics services are extensively employed in various real-time applications, including crime monitoring, business intelligence, and traffic flow control. These applications typically depend on cloud data centers to aggregate video stream tasks using Infrastructure as a Service (IaaS). However, the conventional approach of employing limited-term leased virtual machines (VMs) often leads to leads to high costs and idle time. Serverless computing or Function as a Service (FaaS) offers a more adaptable, pay-as-you-go solution but at a higher price, introducing latency challenges. Edge servers can reduce latency and costs, but limited physical resources affect accuracy. Opting for high-quality analytic services with better frame rates and resolutions can maintain accuracy but may not control costs. Therefore, the key challenge in video analytics is choosing the right configuration and computing methods for high accuracy, low cost, and low latency in edge, IaaS and FaaS environments. To address the challenge, we consider a scenario where the video analytics task is offloaded by combining the above three placements (i.e., VM, serverless, and edge node) to optimize the cost and latency while keeping the accuracy. The optimization problem can be formulated as an Integer Programming (IP) problem of which the NP-hardness is proved. To further deal with it, we propose an efficient online algorithm, which can optimize cost and latency using the Lyapunov optimization analysis while ensuring accuracy over long periods. Finally, the multi-angle simulation experiment results show that OCPA can effectively reduce the cost and delay compared with the benchmarks. At the same time, the achieved accuracy is close to the set long-term time-averaged value.

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