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IEEE International Conference on information Communication and Signal Processing (ICICSP)

作者： Shuo Ma Haitao Xiao Qinyao Li Linkun Ma The Key Laboratory of Intelligent Network and Network Security Ministry of Education China School of Information and Communication Engineering Xi'an Jiaotong University Xi'an China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

In recent years, with the rapid development of wireless communication technology in the military field and the proliferation of the number of various types of wireless communication devices, the battlefield electromagnetic environment has become more and more complex, which puts forward higher requirements for the anti-interference ability of communication systems. In order to enhance the reliability and adaptability of communication systems in complex electromagnetic environments, this paper combines Deep Q-Learning (DQN) with Long Short-Term Memory (LSTM), and proposes a communication anti-jamming intelligent decision- making algorithm based on DQN-LSTM, which realizes adaptive frequency use. In this method, dynamic s-greedy strategy and LSTM are utilized to improve the convergence and stability of DQN. Simulation results show better anti-jamming performance compared to traditional anti-jamming methods and traditional DQN decision-making algorithms.

关键词： Wireless communication Heuristic algorithms Decision making Signal processing algorithms Interference Deep reinforcement learning Stability analysis Electromagnetics Jamming Long short term memory

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Reversible Data Hiding Algorithm in Encrypted Domain Based on Matrix Secret Sharing

Reversible Data Hiding Algorithm in Encrypted Domain Based o...

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Electronics and Communication, network and computer Technology (ECNCT), International Conference on

作者： Zongbao Jiang Minqing Zhang Yan Ke Chao Jiang Key Laboratory of Network and Information Security of PAP Engineering University of PAP Xi 'an China

Focused on the problems of user privacy disclosure and ciphertext data management in cloud service environment, this paper proposes a reversible data hiding algorithm in encrypted domain based on matrix secret sharing. Firstly, the matrix secret sharing of the pixels of the encrypted image was used to obtain n shares, in the process of constructing shared vectors for secret segmentation, the random variables involved in encryption and the location index of ciphertext feedback were used to construct the mapping relationship between them and the secret data to be embedded to generate redundant space for data embedding. Finally, the overflow pixels were processed, and the edge information was embedded by differential extension technology to obtain n marked encrypted images. The experimental results showed that the algorithm has complete reversibility. In contrast to the current algorithms, the embedding rate of the algorithm was greatly improved. The maximized embedding rate of the (3,4)-threshold scheme was 4.00bpp, and the maximized embedding rate of the (5,5)-threshold scheme was 6.40bpp, and the statistical characteristics such as correlation coefficient of the marked encrypted images were consistent with those of random image, and the security was high. The algorithm not only has the functions of privacy data protection and ciphertext authentication management, but also has the disaster recovery feature to realize the multi-party security storage of data.

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SF-DETR: A Road Small Target Detection Model Based on RTDETR

SF-DETR: A Road Small Target Detection Model Based on RTDETR

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International Conference on computer Supported Cooperative Work in Design

作者： Jianhua Gao Jiazheng Man Chunlin Zhao Xiaoming Wu Xiangzhi Liu Huomin Dong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Shandong Shanke Intelligent Technology Co. Ltd

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

As a part of computer vision, object detection is crucial for traffic management, emergency response, autonomous driving vehicles, and smart cities. Despite significant progress in object detection, detecting small objects remains challenging due to their low resolution, which results in little visual information, difficulty in extracting discriminative features, and susceptibility to interference from environmental factors. To address these challenges, we propose SF-DETR, a new model designed specifically for scenarios with small targets. Firstly, We designed a new backbone network that uses partial channel self-attention to replace the backbone network of RTDETR, which can capture both local and global contextual information for extracting and enhancing input features, thereby enhancing the perception of small targets. Secondly, in order to enhance the feature representation ability of the model and better preserve the details of small objects, we proposed Feature Enhancement and Refinement (FER) module, which incorporates a bidirectional fusion mechanism between high-resolution and low-resolution features, allowing for more comprehensive information transfer between features and further improving the effect of multi-scale feature fusion. Finally, we introduce an efficient IoU method (PIoU) which simplifies the computation, speeds up the convergence, and improves the detection accuracy. SF-DETR significantly improves the detection of small targets, outperforming widely used models on various metrics, while significantly reducing model parameters and computational costs compared to RT-DETR. Compared to RTDETR-R34, our model has improved the mAP@50 and mAP@0.5:0.95 by 3.3% and 2.4% respectively on the Visdrone2019 test set.

关键词： Visualization Accuracy Target recognition Smart cities Computational modeling Roads Object detection Feature extraction Computational efficiency Data mining

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A DoS-attack Resilient Control Approach for AIOT-enabled Systems

A DoS-attack Resilient Control Approach for AIOT-enabled Sys...

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2023 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2023

作者： Cao, Xiaoya Chen, Zhenya Liu, Weisheng Liu, Yuanlong Wang, Wenting Wang, Xin Yang, Ming Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China State Grid Shandong Electric Power Company Jinan China State Grid Shandong Electric Power Research Institute Jinan China

ISBN: (纸本)9798350312270

This paper presents a novel controller design for dealing with the challenges posed by Denial-of-Service (DoS) attacks in the context of Artificial Intelligence of Things (AIOT). The proposed design employs a prediction approach that utilizes uniform quantization for both encoder and decoder, aiming to effectively mitigate data loss during data transmission in scenarios involving DoS attacks. Furthermore, the study investigates the application of dynamic system design as a means to enhance the security defense mechanisms of AIOT, consequently bolstering the overall resilience of the system. The effectiveness and practicality of the proposed approach are validated through comprehensive simulations, providing empirical evidence of its efficacy. © 2023 IEEE.

关键词： System stability

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Privacy-Enhanced Role-Based Access Control for IoT Systems

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IEEE Internet of Things Journal 2025年

作者： Zhang, Yawei Chen, Lanxiang Zhu, Yizhao Fujian Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fuzhou350117 China City University of Macau Faculty of Data Science China

Unauthorized accessing private information poses a significant security risk in IoT. Although role-based access control can partially address the problem, there is a lack of studies on protecting the privacy of roles. This work proposes a novel privacy-enhanced role-based access control (PE-RBAC) scheme for resource-limited cloud-based IoT systems. Our PE-RBAC scheme enhances the privacy of traditional RBAC by allowing the cloud platform (CP) to learn only the necessary roles of IoT devices rather than their entire role sets. IoT devices remain unaware of the cloud platform’s roles through the incorporation of the authorized private set intersection (APSI) protocol. Our scheme enables role authorization to prevent unauthorized requests by integrating an authorization center (AC) with an authentication scheme. It achieves lightweight computation and communication overhead using a garbled Bloom filter (GBF), independent of IoT roles. It requires only XOR operations and random number generation, making it ideal for IoT devices with limited computing capacity and bandwidth. Through formal security model definitions and rigorous security analysis, it achieves unlinkability and ciphertext indistinguishability for IoT device privacy and unforgeability for both the cloud platform and IoT devices. It demonstrates high practicality through real-world-like experiments considering varying role sizes and the number of concurrent IoT devices. Experimental results show a throughput of 1,100 requests per second, with storage overhead remaining linear with the number of devices. © 2014 IEEE.

关键词： Cloud platforms

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Research on power grid equipment disaster loss model based on "strong wind and heavy rain"combined disaster 2

Research on power grid equipment disaster loss model based o...

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2nd International Conference on Smart Grids and Energy Systems, SGES 2023

作者： Zhang, Zehao Wang, Tianyu Li, Jingshi Feng, Jie Guan, Cheng Yan, Yiran State Grid Key Laboratory of Information & Network Security State Grid Smart Grid Research Institute Co. LTD Beijing102206 China State Grid Corporation of China Beijing100031 China

ISBN: (纸本)9798350371659

This paper comprehensively considers the evolution characteristics and recurrence laws of urban power grid complex disasters, studies the superimposed amplified damage effects of natural disasters of different causes on power grid equipment in the time domain and the space domain, and builds a multi-physical field dynamic simulation model of complex disasters. Aiming at the coupling mechanism of power grid damage under the simultaneous action of multiple physical fields, such as strong wind and rainstorm load, land seepage, flood and waterlogging, the mechanical modeling is carried out to solve the problem of single factor in power grid disaster loss assessment under complex disaster scenarios, and to lay the theoretical and technical foundation for improving the disaster prevention and mitigation ability of power grid equipment in actual complex environments. © 2023 IEEE.

关键词： Disasters

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An Intelligent Routing Decision-Making Technique Based on DQN for MANETs

An Intelligent Routing Decision-Making Technique Based on DQ...

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IEEE International Conference on information Communication and Signal Processing (ICICSP)

作者： Qinyao Li Haitao Xiao Shuo Ma Linkun Ma The Key Laboratory of intelligent network and network security Ministry of Education China School of Information and Communication Engineering Xi'an Jiaotong University Xi'an China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

The rapid advancement and utilization of Mobile Ad Hoc networks(MANETs) have led to the widespread application of deep learning in intelligent routing issues. While current intelligent routing algorithms concentrate on identifying the best next-hop node through deep learning, they overlook the influence of the dynamic external electromagnetic environment and their own network setting on the routing approach. Consequently, this paper introduces a deep reinforcement learning-based intelligent routing decision-making technique. Tailored to diverse routing approaches needed for various service types and intricate link conditions, this method allows nodes to flexibly adapt their routing strategies in response to real-time service requirements and environmental variations. Through simulations, it is observed that the intelligent routing policy decision-making technique exhibits superior overall performance compared to a single routing policy.

关键词： Deep learning Heuristic algorithms Decision making Signal processing algorithms Signal processing Routing Real-time systems Ad hoc networks Delays Mobile computing

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Multimodal Multi-objective Particle Swarm Optimization Algorithm Based on Multi-directional Guidance 4

Multimodal Multi-objective Particle Swarm Optimization Algor...

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4th International Conference on High Performance Big Data and Intelligent Systems, HDIS 2022

作者： Li, Wei Gao, Yetong Wang, Lei School of Computer Science and Engineering Xi'an University of Technology Xi'an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China

ISBN: (数字)9781665491440

ISBN: (纸本)9781665491440

In multimodal multi-objective optimization, the key issue is to find as many Pareto optimal solutions as possible and select promising solutions in the environmental selection. This paper proposes a multimodal multi-objective particle swarm optimization algorithm based on multi-directional guidance (MM-PSO-MG) to solve these problems. In the proposed algorithm, multi-directional guidance strategy is introduced to avoid premature convergence and find more Pareto optimal solutions. Moreover, the rank-based special crowding distance strategy is used to select promising solutions. 11 multimodal multi-objective test problems are used to verify the performance of the proposed algorithm. The results show that the proposed algorithm is competitive. © 2022 IEEE.

关键词： Multiobjective optimization

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Privacy protection oriented on data stream computing

Privacy protection oriented on data stream computing

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2022 International Conference on computer Application and information security, ICCAIS 2022

作者： He, Renjie Zheng, Xianghan Wang, Gong School of Computer and Big Data Fuzhou University Fujian Fuzhou China The Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fujian Fuzhou China

ISBN: (纸本)9781510663459

Data security has always been one of the public concerns, and homomorphic encryption technology, as one of the effective means to ensure data security, has always been a hot topic of academic research. Traditional machine learning is trained in a batch environment. In practice, data owners tend to outsource data and model training tasks to third-party clouds with huge computing resources, but data outsourcing carries the risk of privacy leakage. Therefore, in order to prevent this situation, an effective method is to encrypt the data before the outsourcing, and the third-party cloud trains the machine model of the ciphertext data. This method has special requirements for encryption algorithms which should support computation directly on the ciphertext data. This paper presents a novel data flow computing privacy protection framework based on the homomorphic encryption algorithm CKKS and the flow data processing engine Flink, called SDPPF (Streaming Data Privacy Protection on Flink). The proposed framework supports the functions of data stream encryption, decryption and ciphertext computing, and expands the functions of vector point multiplication and array sum operation on the basic simultaneous operation supported by the original CKKS algorithm. This paper also selects a classical machine learning algorithm: KNN algorithm, combined with the homomorphic encryption algorithm CKKS to realize the privacy protection machine learning algorithm. © 2023 SPIE.

关键词： Cryptography

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Cross-Chain Identity Authentication Protocol based on Group Signature and Zero-Knowledge Proof

Cross-Chain Identity Authentication Protocol based on Group ...

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International Conference on computer Supported Cooperative Work in Design

作者： Shujiang Xu Duanzhen Li Lianhai Wang Miodrag J. Mihaljević Shuhui Zhang Wei Shao Qizheng Wang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan PR China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan PR China Serbian Academy of Sciences and Arts Mathematical Institute Belgrade Serbia

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Cross-chain interaction plays a crucial role in enhancing asset circulation and data sharing across diverse blockchain systems. Cross-chain identity authentication is the primary pre-requisite of cross-chain security interaction. However, existing cross-chain identity authentication protocol generally has issues such as insufficient decentralization, poor universality, and low authentication efficiency. These issues compromise the reliability of cross-chain interactions. Based on group signature and zero-knowledge proof, this paper proposes a cross-chain identity authentication protocol to address these concerns. The protocol employs Decentralized Identifiers(DIDs) as the global identity identifier of users, utilizes zero-knowledge proof to provide privacy for the verification of users' identities when joining the group, and then constructs the users' transaction signatures through group signatures to hide the users' identities. This approach not only reduces the risk of users' privacy leakage but also facilitates mutual recognition of identities among cross-chain systems. Finally, the security analysis and experimental analysis shows the correctness and efficiency of the proposed scheme.

关键词： Privacy Protocols Federated learning Authentication Blockchains Reliability

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