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6th International Conference on Frontier Technologies of information and computer, ICFTIC 2024

作者： Li, Caiyun Yang, Ming Cao, Jian Wu, Xiaoming He, Yunpeng Wang, Xin Jinan250014 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250014 China Shandong Evayinfo Technology Co. Ltd Jinan250101 China

ISBN: (纸本)9798331541750

Heterogeneous datasets induce local gradient variance and global gradient variance in distributed learning, collectively referred to as gradient heterogeneity. Gradient heterogeneity increases the system's vulnerability to Byzantine attacks, affecting performance. To address this issue, the paper proposes a Gradient Heterogeneity Twofold Optimization (GHTO) algorithm to enhance the Byzantine robustness of the system in non-independent and identically distributed environments. Specifically, a Local Gradient Correction (LGC) algorithm is proposed, which aims to reduce the local gradient variance. By further integrating LGC with momentum and bucketing techniques to achieve dual optimization of local and global gradient variance. Experimental results demonstrate the proposed LGC algorithm diminishes local gradient variance, and the GHTO method optimizes both local and global gradient variance, which further strengthens the robustness of the system against Byzantine attacks. © 2024 IEEE.

关键词： Adversarial machine learning

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

Visual malware classification and detection method based on ...

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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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Self-Evaluation of Large Language Model based on Glass-box Features

Self-Evaluation of Large Language Model based on Glass-box F...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Huang, Hui Qu, Yingqi Liu, Jing Yang, Muyun Xu, Bing Zhao, Tiejun Lu, Wenpeng Faculty of Computing Harbin Institute of Technology Harbin China Baidu Inc. Beijing China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Jinan China

ISBN: (纸本)9798891761681

The proliferation of open-source Large Language Models (LLMs) underscores the pressing need for evaluation methods. Existing works primarily rely on external evaluators, focusing on training and prompting strategies. However, a crucial aspect - model-aware glass-box features - is overlooked. In this study, we explore the utility of glass-box features under the scenario of self-evaluation, namely applying an LLM to evaluate its own output. We investigate various glass-box feature groups and discovered that the softmax distribution serves as a reliable quality indicator for self-evaluation. Experimental results on public benchmarks validate the feasibility of self-evaluation of LLMs using glass-box features. © 2024 Association for Computational Linguistics.

关键词： Open systems

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Convertible certificateless proxy re-signcryption for data access control in cloud computing

Convertible certificateless proxy re-signcryption for data a...

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2023 International Conference on computer Application and information Security, ICCAIS 2023

作者： Wu, Chenhuang Yang, Linghui Yuan, Beibei Li, Huimin Huang, Hui Fujian Key Laboratory of Financial Information Processing Putian University Fujian Putian351100 China Fujian Zhongxin Network Security Information Technology Co. Ltd. Fujian Fuzhou350011 China School of Computer Science Minnan Normal University Fujian Zhangzhou363000 China

ISBN: (纸本)9781510675032

Cloud storage service enables users not only to save their local data storage costs but also to access their outsourced data at any time and anywhere. Traditionally, a data owner outsources the encrypted data to the cloud server and then checks its integrity with the help of cloud auditing strategies. Obviously, in such a manner, only confidentiality and integrity are considered following the above two independent steps. Furthermore, it becomes an issue that the data owner may want to share their outsourced data with some authorized data receivers and convince them that the data are from and the same as the data owner's. To solve this issue, absorbing the advantage of the certificateless cryptosystem, we propose an efficient certificateless proxy re-signcryption (CLPRSC) for data access control in cloud computing. Moreover, the CLPRSC possesses ciphertext verifiable, convertible, and designated re-signcryptable properties with the highest efficiency among the achievable signcryption with proxy re-encryption schemes, which may be of independent interest. © 2024 SPIE.

关键词： Access control

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M2G4RTP: A Multi-Level and Multi-Task Graph Model for Instant-Logistics Route and Time Joint Prediction 39

M2G4RTP: A Multi-Level and Multi-Task Graph Model for Instan...

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39th IEEE International Conference on Data Engineering, ICDE 2023

作者： Cai, Tianyue Wan, Huaiyu Wu, Fan Wen, Haomin Guo, Shengnan Wu, Lixia Hu, Haoyuan Lin, Youfang Beijing Jiaotong University School of Computer and Information Technology Beijing China Cainiao Network Artificial Intelligence Department Hangzhou China Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing China

ISBN: (纸本)9798350322279

Instant-logistics (e.g., food delivery and package pick-up) is increasingly calling for Route and Time Prediction (RTP), which aims to predict both future route and arrival time of a courier's unvisited locations. Accurate RTP can greatly benefit the platform, such as optimizing order dispatching and improving user experience. Although recent years have witnessed various works for solving the RTP problem, they still suffer from the following three limitations: i) Failing to consider the high-level transfer mode of couriers between AOIs (Areas Of Interest, such as residential quarters or office buildings), which can help to build more accurate RTP. ii) Failing to simultaneously make the route and time prediction. Existing works either separately predict route/time or predict them in a two-step way. However, since route and time are strongly correlated (nearby locations in the route should have similar arrival times), jointly predicting them should be more effective. iii) The widely adopted tree-based or sequence-based architecture fails to fully encode the spatial relationship between different locations. To address the above limitations, we propose a multi-level and multi-task graph model, named M2G4RTP, for instant-logistics route and time joint prediction. Specifically, we propose a multi-level graph encoder equipped with a newly-designed GAT-e encoding module to capture couriers' both high-level transfer modes between AOIs and low-level transfer modes between locations. Moreover, a multi-task decoder is presented to jointly predict the route and time at different levels. Finally, a loss weighting method based on homoscedastic uncertainty is designed to balance the two tasks adaptively. Extensive experiments on an industry-scale real-world dataset, as well as the online deployment on Cainiao Alibaba, demonstrate the superiority of our proposed model. © 2023 IEEE.

关键词： Location

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Partial Offloading Strategy Based on Deep Reinforcement Learning in the Internet of Vehicles

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6517-6531页

作者： Tian, Shujuan Zhu, Xinjie Feng, Bochao Zheng, Zhirun Liu, Haolin Li, Zhetao Xiangtan University School of Computer Science School of Cyberspace Security Xiangtan411105 China Xiangtan University Hunan International Scientific and Technological Cooperation Base of Intelligent network Xiangtan411105 China Xiangtan University Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan411105 China Xiangtan University School of Mathematics and Compulational Science Xiangtan China Jinan University College of Information Science and Technology Guangzhou510632 China

Driven by the increasing demands of vehicular tasks, edge offloading has emerged as a promising paradigm to enhance quality of experience (QoE) in Internet of Vehicles (IoV) networks. This approach enables vehicles to offload computation-intensive tasks to edge servers, resulting in reduced computation delays and lower energy consumption. However, traditional binary offloading limits the efficiency of edge offloading. To address this gap, we propose a partial offloading strategy that jointly optimizes the offloading ratio, computation, and communication resources in IoV. Recognizing the varying priorities of vehicular tasks regarding task delay and energy consumption, we formulate two distinct scenarios: one focused on minimizing delay and the other on minimizing energy consumption. Furthermore, we employ a reinforcement learning approach to establish a multi-dimensional joint optimization function by setting different objectives for each scenario. Based on this framework, we introduce a multi-state iteration deep deterministic policy gradient algorithm (SIDDPG), which effectively determines task partitioning and resource allocation. Simulation results demonstrate that the proposed algorithm outperforms benchmark schemes in terms of task delay and energy consumption. © 2002-2012 IEEE.

关键词： Deep reinforcement learning

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A Byzantine Client Detection Method for Federated Learning Based on Encrypted Repeated Median Linear Regression 24

A Byzantine Client Detection Method for Federated Learning B...

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5th International Conference on computer Science and Management Technology, ICCSMT 2024

作者： Guo, Jinlong Yang, Ming Li, Huijuan Wu, Xiaoming He, Yunpeng Wang, Xin Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Shandong Jinan China Shandong Evayinfo Technology Co. Ltd. Shandong Jinan China

ISBN: (纸本)9798400709999

Federated learning is an emerging distributed machine learning technology that allows participants to jointly train machine learning models locally without the need for large-scale data transmission and sharing, thereby greatly improving data security. However, some Byzantine clients may intentionally tamper with local model parameter updates with the intention of manipulating the global model's update direction, which can lead to a decline in the overall performance of federated learning. Besides, existing Byzantine client detection research is conducted in plain text, which will lead to the leakage of private information. To solve the above issues, we propose a Byzantine client detection method for federated learning based on encrypted repeated median linear regression. Specifically, it combines the repeated median linear regression algorithm with homomorphic encryption, which can detect and eliminate clients with Byzantine behavior in the ciphertext state, ensuring the security and reliability of the parameters in the entire federated learning process. Additionally, we also designed an adaptive gradient clipping method based on historical iteration to clip the gradient to a reasonable range, which can not only reduce communication and computing overhead, but also improve the convergence speed and stability of the model. Experiments show that the proposed method can maintain high accuracy under the influence of malicious behavior of Byzantine clients. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Federated learning

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Spatio-Temporal Context Modeling for Road Obstacle Detection 1

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Wu, Xiuen Wang, Tao Liang, Lingyu Li, Zuoyong Ching, Fum Yew College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350108 China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Wuyishan354300 China School of Electronic and Information Engineering South China University of Technology Guangzhou510641 China Ministry of Education Key Laboratory of Computer Network and Information Integration Southeast University Nanjing211189 China Guangzhou510320 China Penang11800 Malaysia

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

Road obstacle detection is an important problem for vehicle driving safety. In this paper, we aim to obtain robust road obstacle detection based on spatio-temporal context modeling. Firstly, a data-driven spatial context model of the driving scene is constructed with the layouts of the training data. Then, obstacles in the input image are detected via the state-of-the-art object detection algorithms, and the results are combined with the generated scene layout. In addition, to further improve the performance and robustness, temporal information in the image sequence is taken into consideration, and the optical flow is obtained in the vicinity of the detected objects to track the obstacles across neighboring frames. Qualitative and quantitative experiments were conducted on the Small Obstacle Detection (SOD) dataset and the Lost and Found dataset. The results indicate that our method with spatio-temporal context modeling is superior to existing methods for road obstacle detection. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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Smoothing Neural network for Non-Lipschitz Optimization with Linear Inequality Constraints

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Chinese Journal of Electronics 2021年第4期30卷 634-643页

作者： YU Xin WU Lingzhen XIE Mian WANG Yanlin XU Liuming LU Huixia XU Chenhua Department of Computer and Electronic Information Guangxi University The Guangxi Key Laboratory of Multimedia Communications and Network Technology School of Computer Science and Engineering Guilin University of Aerospace Technology Department of Electrical Engineering Guangxi University

This paper presents a smoothing neural network to solve a class of non-Lipschitz optimization problem with linear inequality constraints. The proposed neural network is modelled with a differential inclusion equation, which introduces the smoothing approximate techniques. Under certain conditions, we prove that the trajectory of neural network reaches the feasible region in finite time and stays there thereafter, and that any accumulation point of the solution is a stationary point of the original optimization problem. Furthermore, if all stationary points of the optimization problem are isolated,then the trajectory converges to a stationary point of the optimization problem. Two typical numerical examples are given to verify the effectiveness of the proposed neural network.

关键词： Non-Lipschitz optimization Neural networks Stationary points Convergence

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Task Offloading Strategy with Emergency Handling and Blockchain Security in SDN-Empowered and Fog-Assisted Healthcare IoT

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Tsinghua Science and Technology 2022年第4期27卷 760-776页

作者： Junyu Ren Jinze Li Huaxing Liu Tuanfa Qin School of Electronic and Information Engineering South China University of TechnologyGuangzhou 510640China Guangxi University Xingjian College of Science and Liberal Arts Nanning 530004China Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi UniversityNanning 530004China Guangxi Key Laboratory of Multimedia Communications and Network Technology School of ComputerElectronics and InformationGuangxi UniversityNanning 530004China Faculty of Earth Sciences&Geography Trinity CollegeUniversity of CambridgeCambridgeCB21TNUK

With the rapid advancement of the Internet of Things(IoT),the typical application of wireless body area networks(WBANs)based smart healthcare has drawn wide attention from all sectors of *** alleviate the pressing challenges,such as resource limitations,low-latency service provision,mass data processing,rigid security demands,and the lack of a central entity,the advanced solutions of fog computing,software-defined networking(SDN)and blockchain are leveraged in this *** the basis of these solutions,a task offloading strategy with a centralized low-latency,secure and reliable decision-making algorithm having powerful emergency handling capacity(LSRDM-EH)is designed to facilitate the resource-constrained edge devices for task ***,to well ensure the security of the entire network,a comprehensive blockchain-based two-layer and multidimensional security strategy is ***,to tackle the inherent time-inefficiency problem of blockchain,we propose a blockchain sharding scheme to reduce system time *** simulation has been conducted to validate the performance of the proposed measures,and numerical results verify the superiority of our methods with lower time-latency,higher reliability and security.

关键词： wireless body area networks(WBANs) healthcare IoT software-defined networking(SDN) fog computing blockchain task offloading blockchain sharding

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