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Research on active defense decision-making method for cloud boundary networks based on reinforcement learning of intelligent agent

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High-Confidence Computing 2024年第2期4卷 50-61页

作者： Huan Wang Yunlong Tang Yan Wang Ning Wei Junyi Deng Zhiyan Bin Weilong Li School of Computer Science and Technology Guangxi University of Science and TechnologyLiuzhou 545000China Liuzhou Key Laboratory of Big Data Intelligent Processing and Security Liuzhou 545000China Guangxi Education System Network Security Monitoring Center Liuzhou 545000China Center of Graphics and Information Guangxi ECO-Engineering Vocational&Technical CollegeLiuzhou 545004China

The cloud boundary network environment is characterized by a passive defense strategy,discrete defense actions,and delayed defense feedback in the face of network attacks,ignoring the influence of the external environment on defense decisions,thus resulting in poor defense ***,this paper proposes a cloud boundary network active defense model and decision method based on the reinforcement learning of intelligent agent,designs the network structure of the intelligent agent attack and defense game,and depicts the attack and defense game process of cloud boundary network;constructs the observation space and action space of reinforcement learning of intelligent agent in the non-complete information environment,and portrays the interaction process between intelligent agent and environment;establishes the reward mechanism based on the attack and defense gain,and encourage intelligent agents to learn more effective defense *** designed active defense decision intelligent agent based on deep reinforcement learning can solve the problems of border dynamics,interaction lag,and control dispersion in the defense decision process of cloud boundary networks,and improve the autonomy and continuity of defense decisions.

关键词： Active defense decision-making Cloud boundary network security Intelligent agent reinforcement learning Offensive and defensive game

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StarMFND: Star Topology Multi-Domain Fake News Detection 7

StarMFND: Star Topology Multi-Domain Fake News Detection

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7th International Conference on Data Science and information Technology, DSIT 2024

作者： Gu, Pengfei Chen, Yibing Bing, Wanlong Liu, Yahui Ma, Hongliang Wang, Wei Shihezi University School of Information Science and Technology Shihezi China Xinjiang Production and Construction Corps Key Laboratory of Computing Intelligence and Network Information Security Shihezi China Beijing Jiaotong University School of Computer Science and Information Technology Beijing China

ISBN: (纸本)9798350384093

The proliferation of the Internet and the emergence of social media platforms have greatly accelerated the speed and scope of information dissemination, while also introducing the peril of fake news propagation, particularly in political, economic, health, and other domains. Many scholars have conducted research on fake news detection, and most of this research focuses on domain-specific single-domain fake news detection or mixed-domain detection without considering domain information. Most existing multi-domain fake news detection approaches model the domains of fake news implicitly and do not pay attention to the domain seesaw phenomenon. To solve the above problems, this paper proposes a Star Topology Multi-Domain Fake News Detection Model (StarMFND), which explicitly models the domains of fake news using a star topology, and simultaneously leverages the data from all the domains to obtain the common and specific features associated with fake news across different domains. Experiments on Chinese and English datasets demonstrated the good performance of StarMFND for multi-domain fake news detection. © 2024 IEEE.

关键词： Fake News Detection Multi-Domain Learning Multi-Task Learning Social Media

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A Technique for Ensured Cross Chain IBC Transactions Using TPM 2

A Technique for Ensured Cross Chain IBC Transactions Using T...

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2nd International Conference on Big Data and Privacy Computing, BDPC 2024

作者： Wang, YiChuan Yan, YiLiang Liu, XiaoXue Gao, Wen Wang, ZiYan 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: (纸本)9798350307597

Cross chain contact across several blockchains becomes increasingly significant in importance with the increased use of blockchain technology. However, security cannot be assured when producing equivalent outputs through cross-chain transmission because it is simple to launch an assault, and without security measures;it is simple to cause the transmission material to be stolen. This article combines TPM, IBC, and associates encryption and decryption techniques for protection to overcome this issue. We have establishes the viability and efficacy of the suggested method through testing and performance analyses. The experimental findings show that our approach can successfully preserve privacy and guarantee the security of data transfer. © 2024 IEEE.

关键词： Blockchain

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CDR2IMG:A Bridge from Text to Image in Telecommunication Fraud Detection

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computer Systems Science & Engineering 2023年第10期47卷 955-973页

作者： Zhen Zhen Jian Gao School of Information Network Security People’s Public Security University of ChinaBeijing100038China Key Laboratory of Safety Precautions and Risk Assessment Ministry of Public SecurityBeijing102623China

Telecommunication fraud has run rampant recently ***,previous studies depend highly on expert knowledge-based feature engineering to extract behavior information,which cannot adapt to the fastchanging modes of fraudulent ***,we propose a new taxonomy that needs no hand-designed features but directly takes raw Call DetailRecords(CDR)data as input for the ***,we proposed a fraud detectionmethod using a convolutional neural network(CNN)by taking CDR data as images and applying computer vision techniques like image *** experiments on the real-world dataset from the 2020 Digital Sichuan Innovation Competition show that our proposed method outperforms the classic methods in many metrics with excellent stability in both the changes of quantity and the balance of *** with the state-of-the-art method,the proposed method has achieved about 89.98%F1-score and 92.93%AUC,improving 2.97%and 0.48%,*** the augmentation technique,the model’s performance can be further enhanced by a 91.09%F1-score and a 94.49%AUC *** telecommunication fraud detection,our method can also be extended to other text datasets to automatically discover new features in the view of computer vision and its powerful methods.

关键词： Telecommunication fraud detection call detail records convolutional neural network

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Few-Shot Semantic Dependency Parsing via Graph Contrastive Learning 30

Few-Shot Semantic Dependency Parsing via Graph Contrastive L...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Li, Bin Fan, Yunlong Sataer, Yikemaiti Shi, Chuanqi Gao, Miao Gao, Zhiqiang School of Computer Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Ministry of Education China

ISBN: (纸本)9782493814104

Graph neural networks (GNNs) have achieved promising performance on semantic dependency parsing (SDP), owing to their powerful graph representation learning ability. However, training a high-performing GNN-based model requires a large amount of labeled data and it is prone to over-fitting in the absence of sufficient labeled data. To address this drawback, we propose a syntax-guided graph contrastive learning framework to pre-train GNNs with plenty of unlabeled data and fine-tune pre-trained GNNs with few-shot labeled SDP data. Through extensive experiments conducted on the SemEval-2015 Task 18 English dataset in three formalisms (DM, PAS, and PSD), we demonstrate that our framework achieves promising results when few-shot training samples are available. Furthermore, benefiting from the pre-training process, our framework exhibits notable advantages in the out-of-domain test sets. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Graph neural networks

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Privacy-Preserving Multi-keyword Fuzzy Adjacency Search Strategy for Encrypted Graph in Cloud Environment

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computers, Materials & Continua 2024年第3期78卷 3177-3194页

作者： Bin Wu Xianyi Chen Jinzhou Huang Caicai Zhang Jing Wang Jing Yu Zhiqiang Zhao Zhuolin Mei School of Computer and Big Data Science Jiujiang UniversityJiujiang332005China Jiujiang Key Laboratory of Network and Information Security Jiujiang332005China School of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China School of Computer Engineering Hubei University of Arts and ScienceXiangyang441053China School of Modern Information Technology Zhejiang Institute of Mechanical and Electrical EngineeringHangzhou310053China Information Center Jiangxi Changjiang Chemical Co.Ltd.Jiujiang332005China School of Mathematics and Computer Science Ningxia Normal UniversityGuyuan756099China

In a cloud environment,outsourced graph data is widely used in companies,enterprises,medical institutions,and so *** owners and users can save costs and improve efficiency by storing large amounts of graph data on cloud *** on cloud platforms usually have some subjective or objective attacks,which make the outsourced graph data in an insecure *** issue of privacy data protection has become an important obstacle to data sharing and *** to query outsourcing graph data safely and effectively has become the focus of *** query is a basic and frequently used operation in graph,and it will effectively promote the query range and query ability if multi-keyword fuzzy search can be supported at the same *** work proposes to protect the privacy information of outsourcing graph data by encryption,mainly studies the problem of multi-keyword fuzzy adjacency query,and puts forward a *** our scheme,we use the Bloom filter and encryption mechanism to build a secure index and query token,and adjacency queries are implemented through indexes and query tokens on the cloud *** proposed scheme is proved by formal analysis,and the performance and effectiveness of the scheme are illustrated by experimental *** research results of this work will provide solid theoretical and technical support for the further popularization and application of encrypted graph data processing technology.

关键词： Privacy-preserving adjacency query multi-keyword fuzzy search encrypted graph

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A Quantum Temporal Difference Learning Method Based on Quantum World Model

A Quantum Temporal Difference Learning Method Based on Quant...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zeng, Peigen He, Ying Yu, F. Richard Du, Jianbo Shenzhen University College of Computer Science and Software Engineering Shenzhen China Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security Xi'an China Carleton University School of Information Technology Ottawa Canada

ISBN: (纸本)9798350351255

Based on quantum parallelism theory and quantum phenomena such as superposition and entanglement, quantum reinforcement learning (QRL) has the potential to surpass classical reinforcement learning (RL). Although some excellent works have been done on QRL, existing RL methods encounter chanllenges when performing in environments with sparse rewards. In this paper, we provide a new perspective on conducting temporal difference (TD) learning in quantum computing, which can eliminate redundant exploration steps compared to classical methods. Specifically, we first use environment information to construct a world model with quantum circuits, enabling it to interact in a quantum way. Then, we perform the learning process by using the quantum world model and Grover's algorithm to query backwards how to reach the recorded states with high TD-errors. Simulation results show that our proposed method has superior performance compared to classical RL algorithms. © 2024 IEEE.

关键词： Adversarial machine learning

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Unsigned Road Incidents Detection Using Improved RESNET From Driver-View Images

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1203-1216页

作者： Li, Changping Wang, Bingshu Zheng, Jiangbin Zhang, Yongjun Chen, C.L. Philip Northwestern Polytechnical University School of Software Xi’an710129 China Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen518060 China Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China South China University of Technology School of Computer Science and Engineering Guangzhou510641 China

Frequent road incidents cause significant physical harm and economic losses globally. The key to ensuring road safety lies in accurately perceiving surrounding road incidents. However, the highly dynamic nature of traffic introduces significant challenges, particularly in detecting sudden and temporary incidents. In this article, we propose a novel detection framework, multihead attention ResNet with dynamic bottleneck (DB_RESNET_MHA), to identify physical unsigned road incidents. Our approach introduces three key innovations. First, we develop a tailored data augmentation strategy to create images that closely mimic the complex variations found in real-world road environments. Second, we enhance model expressiveness by employing attention mechanisms to nonlinearly integrate convolutional kernels within the residual network. Furthermore, we refine the prediction head by applying spatially distinct attention weights, enabling the model to capture intricate correlations between different features more effectively. To demonstrate the effectiveness of our method, we create a dataset for unsigned road incidents (UnsignRI), comprising a total of 16 323 images that capture 12 distinct types of incidents. It stands out as the most comprehensive dataset in the field, encompassing a wide range of geographical features and incident categories. Experimental results show that DB_RESNET_MHA achieves an average accuracy of 96.2% and a f1-score of 0.955 across various categories of unsigned incidents, surpassing other models. © 2020 IEEE.

关键词： Highway accidents

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Distributed acoustic sensing signal event recognition and localization based on improved YOLOv7

Distributed acoustic sensing signal event recognition and lo...

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2023 International Congress on Ultrasonics, ICU Beijing 2023

作者： Zhang, Hongyu Wang, Chang Zhang, Faxiang Jiang, Shaodong Sun, Zhihui Wang, Xiaodong Duan, Zhenhui Gao, Fengxia Liu, Zhaoying 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 Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

The distributed acoustic sensing (DAS) system based on phase-sensitive optical time domain reflection (Φ -OTDR) technology is widely used in pipeline safety monitoring, perimeter security, structure monitoring, etc. Accurate localization and recognition of multiscene events over long distances has always been a challenge. This paper proposes an improved YOLOv7 algorithm for multi-event real-time detection of DAS system. The algorithm employs space-to-depth Conv(SPD-Conv) to replace the strided convolutions and pooling operations in YOLOv7, reducing fine-grained information loss and learning of inefficient feature representations. In addition, the Convolutional Block Attention Module (CBAM) is introduced in YOLOv7 to improve the model performance. We collected spatial-temporal signal data for six types of pipeline safety events, and passed them into the improved YOLOv7 algorithm in the form of data matrixes for training and *** have shown that the proposed method achieves an mAP@.5 (mean Average Precision) of 99.7% for the identification of six pipeline safety event types. Positioning loss reduced to 0.2%,and detection speed can reach 70 frames per second(FPS). Our scheme achieves significant improvements in localization and classification accuracy compared to Faster R-CNN, etc. The event recognition localization method proposed in this paper has the advantage of fast speed and high accuracy. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Light reflection

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A novel firmware secure transmission scheme based on SM2 threshold key agreement algorithm

A novel firmware secure transmission scheme based on SM2 thr...

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2023 International Conference on computer Graphics, Artificial Intelligence, and Data Processing, ICCAID 2023

作者： Wang, Kun Gao, Shaofeng Chen, Xinze Fu, Yong 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 Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9781510677685

During the process of embedded firmware upgrade, to protect the security of the firmware during transmission, this paper proposes an improved firmware transmission scheme based on the combination of the SM2 threshold key agreement algorithm. Between the server, client, and downloader, the SM2 key agreement algorithm is used to generate session key pairs to encrypt the firmware information, and the method of reinforcing and verifying re-transmission is adopted to ensure data security. In order to improve the security of the algorithm, a threshold algorithm is used in the key agreement process. The server acts as a trusted center to execute the threshold algorithm and divide the main secret private keys dA and dB. During the phase of key exchange, at least t+1 sub-secrets are required to recover the complete main secret. This paper successfully implements and verifies the feasibility of this scheme. © 2024 SPIE.

关键词： Firmware

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