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Visual malware classification and detection method based on convolutional neural network 9

Visual malware classification and detection method based on ...

引用

9th ieee international conference on Data science in Cyberspace, DSC 2024

作者： Huang, Hua Sheng, Hua Han, Xingwang Zhang, Wenbin Zhao, Zilin State Grid Shandong Electric Power Company Information and Telecommunications Company Jinan China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China

ISBN: (纸本)9798350391367

With the rapid development of Internet technology, leading to the evolution of malware variants, and malicious threats are also increasing. Therefore, rapid and accurate classification of malware becomes crucial. In response to this challenge, this paper proposes a malware classification method based on deep learning and visualization techniques. The method first extracts sample features, then converts malware binary samples into image format, thus transforming abstract binary data into images with visual information. Subsequently, color mapping technique is applied to convert the processed images into color images. Furthermore, to address imbalanced datasets, this paper introduces data augmentation methods to ensure more balanced and comprehensive training of the model across different sample categories. Finally, an improved convolutional neural network architecture is employed for the classification detection of malware families, achieving accurate classification and identification of different malware families through learning and analysis of sample features. To evaluate the effectiveness of the proposed method, experiments are conducted on the Google Code Jam (GCJ) benchmark dataset. The experimental results demonstrate an accuracy rate of 99.29% on the GCJ dataset. Additionally, we also compared the performance of the improved method before and after, and found that the proposed method can provide more comprehensive feature representation and significantly improve classification accuracy and detection efficiency. © 2024 ieee.

关键词： Convolutional neural networks

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Learning-Driven Zero Trust in Distributed computing Continuum Systems

Learning-Driven Zero Trust in Distributed Computing Continuu...

引用

2023 ieee international conference on Dependable, Autonomic and Secure computing, 2023 international conference on Pervasive Intelligence and computing, 2023 international conference on Cloud and Big Data computing, 2023 international conference on Cyber science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Murturi, Ilir Donta, Praveen Kumar Pujol, Victor Casamayor Morichetta, Andrea Dustdar, Schahram Distributed Systems Group Tu Wien Vienna1040 Austria

ISBN: (纸本)9798350304602

Converging Zero Trust (ZT) with learning techniques can solve various operational and security challenges in Distributed computing Continuum Systems (DCCS). Implementing centralized ZT architecture is seen as unsuitable for the computing continuum (e.g., computing entities with limited connectivity and visibility, etc.). At the same time, implementing decentralized ZT in the computing continuum requires understanding infrastructure limitations and novel approaches to enhance resource access management decisions. To overcome such challenges, we present a novel learning-driven ZT conceptual architecture designed for DCCS. We aim to enhance ZT architecture service quality by incorporating lightweight learning strategies such as Representation Learning (ReL) and distributing ZT components across the computing continuum. The ReL helps to improve the decision-making process by predicting threats or untrusted requests. Through an illustrative example, we show how the learning process detects and blocks the requests, enhances resource access control, and reduces network and computation overheads. Lastly, we discuss the conceptual architecture, processes, and provide a research agenda. © 2023 ieee.

关键词： Distributed computer systems

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SharDAG: Scaling DAG-Based Blockchains Via Adaptive Sharding 40

SharDAG: Scaling DAG-Based Blockchains Via Adaptive Sharding

引用

40th ieee international conference on Data engineering, ICDe 2024

作者： Cheng, Feng Xiao, Jiang Liu, Cunyang Zhang, Shijie Zhou, Yifan Li, Bo Li, Baochun Jin, Hai School of Computer Science and Technology Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab China Hong Kong University of Science and Technology Hong Kong University of Toronto Canada

ISBN: (纸本)9798350317152

Directed Acyclic Graph (DAG)-based blockchain (a.k.a distributed ledger) has become prevalent for supporting highly concurrent applications. Its inherent parallel data structure accelerates block generation significantly, shifting the bottleneck from performance to storage scalability. An intuitive solution is to apply state sharding that divides the entire ledger (i.e., transactions and states) into multiple shards. While each node only stores proportional transactions, it suffers from the challenges of storing and ensuring the processing consistency of cross-shard transactions. In this paper, we propose SharDAG, a new mechanism that leverages adaptive sharding for DAG-based blockchains to achieve high performance and strong consistency. The key idea of SharDAG is to exploit unique characteristics -silent assets -and design a lightweight processing mechanism based on avatar account caching. Furthermore, we design a Byzantine resilient cross-shard verification mechanism with a theoretically optimal number of participating nodes, which guarantees the consistency and security of avatar account aggregation. Our comprehensive evaluations on real-world workloads demonstrate that SharDAG presents up to 3.8 x throughput improvement compared to the state-of-the-art and reduces the storage overhead of cross-shard transactions. © 2024 ieee.

关键词： Blockchain

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A Method for Defects Inspection of Power Transmission Lines Based on DeTA and Integrated Improvement Algorithm 11

A Method for Defects Inspection of Power Transmission Lines ...

引用

11th Joint international Information Technology and Artificial Intelligence conference, ITAIC 2023

作者： Wang, Jin Wang, Bo Wu, Peng Zhang, Guoliang Chen, Jiangqi Zhang, Xi State Grid Smart Grid Research Institute Co.Ltd State Grid Laboratory of Grid Advanced Computing and Applications Beijing China

ISBN: (纸本)9798350333664

Collecting images through drone and helicopter aerial photography is one of the main methods for transmission line inspection. Currently, the popular intelligent transmission defects recognition is mainly based on deep neural network object detection algorithms. Due to the outstanding performance in various public datasets and rapid development of DeTR algorithms, there is a promising application prospect in the field of transmission inspection. This article proposes a transmission line inspection model based on DeTA, which is optimized by integrated improvement algorithm, which inlcudes SNIP sampling algorithm, multi-scale inference strategy, and prior knowledge based box-selection algorithm. experiments have shown that the method proposed in this article has the advantages of accurate defect localization, high detection accuracy, and strong scalability. © 2023 ieee.

关键词： Deep Learning defect recognition DeTA DeTR object detection transmission inspection

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Development Arena through Cloud computing Technology 5

Development Arena through Cloud Computing Technology

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5th ieee international conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Jain, Archana Bansal, Nidhi Pandey, Mahima Shankar Kumar, Gaurav Sharma, Manoj Kumar Malik, Suraj Maurya, Santosh Kumar Iimt University School of Engineering and Technology Department of Computer Science and Engineering Meerut India Sharda University School of Engineering and Technology Computer Science and Engineering Department Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350383522

Latest measurements correlated to the cloud computing technology, found to be very unreliable. For smooth conduction of cloud technology, the report is getting more than 100 values i.e., being added to the cost of the essential component that has been prepared for succession of the technology. Moreover, with the care of cloud computing technology, very intelligent operational programing has been into running, yet the real parameter of performance measurement is showing the actual output. With tremendous literature survey and feedback, the paper has been able to lay out logical terms about the services availed from cloud side. Trendy services involve trust and reliable factors in terms of benefits accruing from the customer side to the service provider. By offering the combined efforts of public and private cloud, business drivers are moving forward with the freedom to deliver their most effective service to the users and are also able to achieve higher spend. © 2024 ieee.

关键词： benefitted primary and secondary services Cloud computing Cloud Service Providers issues Quality measuring factors

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A multi-level and multi-agent collaborative control platform for city-scale virtual power plants based on a cloud-pipe-edge-end fusion architecture 4

A multi-level and multi-agent collaborative control platform...

引用

4th international Signal Processing, Communications and engineering Management conference, ISPCeM 2024

作者： Baorong, Zhou Jiangnan, Li Wenmeng, Zhao Tao, Wang Biao, Zhao Wenyuan, Zheng Guanglei, Huang Mingyu, Ou Electric Power Research Institute China Southern Grid Guangzhou510530 China Shenzhen Power Supply Bureau Co. Ltd. Shenzhen518000 China Tsinghua University Shenzhen International Graduate School Shenzhen518000 China

ISBN: (纸本)9798331528676

With the continuous expansion and complexity of urban power grids, traditional power control methods are difficult to meet the needs of multi-agent, multi-energy and multi-level in modern power grids. To address this problem, this paper proposes a multi-level and multi-agent collaborative control platform for urban-scale virtual power plants based on a cloud-pipe-edge-end fusion architecture. By introducing technologies such as cloud computing, edge computing and the Internet of Things, the platform designs a multi-level control architecture that includes functional modules such as physical information modeling, resource aggregation optimization, automatic power control and instruction decomposition. The innovation of the platform lies in its ability to enable the full process of adjustable resources on the load side of the virtual power plant to participate in the automatic generation control (AGC) and automatic voltage control (AVC) of the power grid, and integrates intelligent monitoring and traceability technology of electric carbon, providing real-time and efficient power control and carbon emission management. Through actual case analysis and verification, the platform is significantly superior to traditional systems in terms of supply and demand balance, response speed, energy efficiency optimization and carbon emission management, which improves the overall operation efficiency and stability of the power grid and provides strong technical support for the intelligent management of urban power grids. ©2024 ieee.

关键词： Cloud platforms

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A Cooperative Repair Scheme for MSR Codes with Two erasures and Small Finite Field 23

A Cooperative Repair Scheme for MSR Codes with Two Erasures ...

引用

23rd ieee international conference on Communication Technology, ICCT 2023

作者： Liu, Yajuan Cai, Han Tang, Xiaohu Southwest Jiaotong University The Information Security and National Computing Grid Laboratory Chengdu China

ISBN: (纸本)9798350325959

In large-scale distributed storage systems, the repair bandwidth, i.e., the amount of data downloaded for failures, is one of the most important factors that determines the performance of the system. In this paper, we design a novel cooperative repair scheme, specifically, for Zigzag MSR codes with optimal repair bandwidth, enabling the repair of any h=2 failed nodes. To the best of our knowledge, this is the first cooperative repair scheme for Zigzag MSR codes. In contrast to the previous cooperative repair MSR codes, our scheme extremely reduces the size of finite field to Fq, q ≥ n+1. © 2023 ieee.

关键词： cooperative repair model distributed storage systems optimal repair bandwidth Zigzag MSR codes

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A Transductive Graph Neural Network learning for grid Resilience Analysis 14

A Transductive Graph Neural Network learning for Grid Resili...

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14th ieee international conference on Communications, Control, and computing Technologies for Smart grids, SmartgridComm 2023

作者： Gautam, Priyanka Sreejith, Amulya Natarajan, Balasubramaniam Kansas State University Electrical and Computer Engineering Manhattan United States

ISBN: (纸本)9781665455541

Power grids are critical infrastructures that require robust resilience analysis to ensure reliable and uninterrupted electricity supply. Traditional simulation-based methods for grid resilience analysis suffer from computational complexity and limited ability to capture the full spectrum of potential disruptions. This paper presents a novel approach to enhance grid resilience by leveraging transductive graph neural network (GNN) learning to identify critical nodes and links. By leveraging the graph structure and system features, GNNs effectively learn resilience metrics and accurately identify critical nodes based on actual grid operational behavior. The efficacy of the proposed approach is demonstrated through case studies on node criticality scoring and critical node/line identification in cascading outage scenarios. The results highlight the advantages of learning-based methods over traditional simulation-based approaches and their potential to revolutionize grid resilience analysis. The contributions of this paper include a graph-based scalable approach for fast cascading analysis, an inductive formulation for training GNN models, and a transfer learning-based approach to scale the model to large-scale power systems. © 2023 ieee.

关键词： Graphic methods

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Lightweight Wildfire Location and Recognition for enhancing Resilience in energy Internet Systems 8

Lightweight Wildfire Location and Recognition for Enhancing ...

引用

8th ieee international conference on energy Internet, ICeI 2024

作者： Wang, Dezhi Liu, Minghui Jiang, Hai Yu, Xin Wu, Hanying Luan, Yajun Dandong Power Supply Company State Grid Liaoning Electric Power Co. Ltd Dandong China

ISBN: (纸本)9798331523558

Wildfires pose significant threats to energy internet and critical infrastructure, including power transmission lines, necessitating rapid and accurate detection to prevent extensive damage. However, implementing real-time wildfire smoke detection on embedded edge devices with limited computing power presents a considerable challenge due to their constrained resources. To address this problem, we propose a lightweight target detection model based on the YOLO framework, specifically designed for real-time mountain fire smoke detection on embedded devices. By simplifying the network structure of YOLOv5-s, particularly in the feature fusion and detection head components, our model reduces computational demand while enhancing performance. Compared to the original YOLOv5-s, our approach nearly doubles the parameter count and significantly improves recall and accuracy. experimental results demonstrate that our lightweight model maintains high levels of accuracy and recall, achieving real-time monitoring capabilities on embedded edge devices, making it an effective solution for early wildfire detection in resource-constrained environments. © 2024 ieee.

关键词： Premixed flames

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Reliability Test of Control Loop Information in New Power Load Management System 5

Reliability Test of Control Loop Information in New Power Lo...

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5th international conference on Information science, Parallel and Distributed Systems, ISPDS 2024

作者： Wang, Shuyang Guo, Qiang Chen, Ke Yu, Haoyang Ren, Mingyuan Cao, Haoxiang Beijing Key Laboratory of Demand Side Beijing China State Grid Changyi Electric Power Supply Company Weifang China State Grid Yichun Electric Power Supply Company Yichun China State Grid Gansu Electric Power Company Lanzhou China North China Electric Power University School of Electric and Electronic Engineering Beijing China

ISBN: (纸本)9798350352719

This paper discusses the application of stochastic forest in the detection of new power load management system, and deals with regression and classification problems by integrating multiple decision tree subtrees. Since each decision tree produces one result, when the number of input samples is N, N classification results will be produced. Finally, all classification results will be selected through a voting mechanism. Random forest algorithm combines Bagging idea and decision tree, where a single decision tree as a weak classifier has the defect of weak generalization ability, but the integrated model becomes a strong classifier. This ensemble learning approach improves the accuracy of classification and prediction, but at the expense of interoperability, i.e. the difficulty of determining decisive variables. Improve the performance of the overall model by combining multiple base models. CART decision trees determine which attribute should be given a higher weight based on the minimum Gini index, which provides a measure of descriptive impurity content and is calculated in a way designed to optimize the tree's performance. © 2024 ieee.

关键词： Stochastic systems

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