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Efficient Penetration Testing Path Planning Based on Reinforcement Learning with Episodic Memory

Computer Modeling in Engineering & Sciences 2024年第9期140卷 2613-2634页

作者： Ziqiao Zhou Tianyang Zhou Jinghao Xu Junhu Zhu Henan Key Laboratory of Information Security National Engineering Technology Research Center of the Digital Switching SystemZhengzhou450000China School of Cryptographic Engineering Information Engineering UniversityZhengzhou450000China

Intelligent penetration testing is of great significance for the improvement of the security of information systems,and the critical issue is the planning of penetration test *** view of the difficulty for attackers to obtain complete network information in realistic network scenarios,Reinforcement Learning(RL)is a promising solution to discover the optimal penetration path under incomplete information about the target *** RL-based methods are challenged by the sizeable discrete action space,which leads to difficulties in the ***,most methods still rely on experts’*** address these issues,this paper proposes a penetration path planning method based on reinforcement learning with episodic ***,the penetration testing problem is formally described in terms of reinforcement *** speed up the training process without specific prior knowledge,the proposed algorithm introduces episodic memory to store experienced advantageous strategies for the first ***,the method offers an exploration strategy based on episodic memory to guide the agents in *** design makes full use of historical experience to achieve the purpose of reducing blind exploration and improving planning ***,comparison experiments are carried out with the existing RL-based *** results reveal that the proposed method has better convergence *** running time is reduced by more than 20%.

关键词： Intelligent penetration testing penetration testing path planning reinforcement learning episodic memory exploration strategy

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A Temporal Knowledge Graph Embedding Model Based on Variable Translation

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Tsinghua Science and technology 2024年第5期29卷 1554-1565页

作者： Yadan Han Guangquan Lu Shichao Zhang Liang Zhang Cuifang Zou Guoqiu Wen Key Lab of Education Blockchain and Intelligent Technology Ministry of EducationGuangxi Normal UniversityGuilin 541004China and also with Guangxi Key Lab of Multi-Source Information Mining and SecurityGuangxi Normal UniversityGuilin 541004China

Knowledge representation learning(KRL)aims to encode entities and relationships in various knowledge graphs into low-dimensional continuous *** is popularly used in knowledge graph completion(or link prediction)***-based knowledge representation learning methods perform well in knowledge graph completion(KGC).However,the translation principles adopted by these methods are too strict and cannot model complex entities and relationships(i.e.,N-1,1-N,and N-N)***,these traditional translation principles are primarily used in static knowledge graphs and overlook the temporal properties of triplet ***,we propose a temporal knowledge graph embedding model based on variable translation(TKGE-VT).The model proposes a new variable translation principle,which enables flexible transformation between entities and relationship ***,this paper considers the temporal properties of both entities and relationships and applies the proposed principle of variable translation to temporal knowledge *** conduct link prediction and triplet classification experiments on four benchmark datasets:WN11,WN18,FB13,and *** model outperforms baseline models on multiple evaluation metrics according to the experimental results.

关键词： knowledge graph knowledge graph completion variable translation temporal properties link prediction triplet classification

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Class activation regularization-based facial emotion recognition network and its application in students’ emotional engagement assessment

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IEEE Transactions on Affective Computing 2024年第2期16卷 1044-1055页

作者： Xu, Luhui Gan, Yanling Jin, Yi The Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University China

Students’ emotional engagement in the classroom can be evaluated through their facial emotions. However, facial emotion recognition remains challenging due to the uncertainty problem, which is routinely caused by factors such as low resolution, intra-class variability, and inter-class similarity. To deal with this problem, we propose a class activation regularization network, which includes two main modules: class activation dropping module (CADM) and hard sample mining module (HSMM). Our key idea is to perform consistency learning between the original images and class-uncertain regions, thereby enabling models to perceive uncertainty of emotional semantics. In CADM, the class-specific attention is obtained using class activation mapping. Then, the attention is partly discarded by imitating dropout mechanism to obtain class-uncertain regions. In HSMM, the hard samples with high uncertainties are identified based on the class-uncertain regions and confidence thresholds. For the hard samples, regularization loss is applied to align the probability distributions between the original images and the class-uncertain regions, thereby improving the network’s awareness of emotional semantics and its ability to handle uncertainty. Extensive experiments on three datasets demonstrate the competitiveness of the proposed method. In addition, our method can be deployed in the classroom for students’ emotional engagement assessment. © 2010-2012 IEEE.

关键词： Students

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Graph neural network based approach Che upo to automatically assigning common weakness enumeration identifiers for vulnerabilities

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Cybersecurity 2024年第3期7卷 1-15页

作者： Peng Liu Wenzhe Ye Haiying Duan Xianxian Li Shuyi Zhang Chuanjian Yao Yongnan Li Key Lab of Education Blockchain and Intelligent Technology Ministry of EducationGuangxi Normal UniversityGuilin 541004China School of Software Beihang UniversityBeijing 100000China School of National Security People's Public Security University of ChinaBeijing 1000000China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal UniversityGuilin 541004China

Vulnerability reports are essential for improving software security since they record key information on *** a report,CWE denotes the weakness of the vulnerability and thus helps quickly understand the cause of the ***,CWE assignment is useful for categorizing newly discovered *** this paper,we propose an automatic CwE assignment method with graph neural ***,we prepare a dataset that contains 3394 real world vulnerabilities from Linux,OpenSSL,Wireshark and many other software ***,we extract state-ments with vulnerability syntax features from these vulnerabilities and use program slicing to slice them according to the categories of syntax *** top of slices,we represent these slices with graphs that characterize the data dependency and control dependency between ***,we employ the graph neural networks to learn the hidden information from these graphs and leverage the Siamese network to compute the similarity between vulnerability functions,thereby assigning CWE IDs for these *** experimental results show that the proposed method is effective compared to existing methods.

关键词： Vulnerability categorization CWE Graph representation GNN

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Robust Hashing with Deep Features and Meixner Moments for Image Copy Detection

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ACM Transactions on Multimedia Computing, Communications and Applications 2024年第12期20卷 1-23页

作者： Yu, Mengzhu Tang, Zhenjun Liang, Xiaoping Zhang, Xianquan Li, Zhixin Zhang, Xinpeng Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University Guilin China School of Computer Science Fudan University Shanghai China

Copy detection is a key task of image copyright protection. Most robust hashing schemes do not make satisfied performance of image copy detection yet. To address this, a robust hashing scheme with deep features and Meixner moments is proposed for image copy detection. In the proposed hashing, global deep features are extracted by applying tensor Singular Value Decomposition (t-SVD) to the three-order tensor constructed in the DWT domain of the feature maps calculated by the pre-trained VGG16. Since the feature maps in the DWT domain are slightly disturbed by digital operations, the constructed three-order tensor is stable and thus the desirable robustness is guaranteed. Moreover, since t-SVD can decompose a three-order tensor into multiple low-dimensional matrices reflecting intrinsic structure, the global deep feature calculation from the low-dimensional matrices can provide good discrimination. Local features are calculated by the block-based Meixner moments. As the Meixner moments are resistant to geometric transformation and can efficiently discriminate various images, the use of the block-based Meixner moments can make discriminative and robust local features. Hash is ultimately determined by quantifying and combining global deep features and local features. The results of extensive experiments on open image datasets demonstrate that the proposed robust hashing outperforms some state-of-the-art robust hashing schemes in terms of classification and copy detection performances. © 2024 Copyright held by the owner/author(s).

关键词： Tensors

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security analysis and secured access design for networks of image remote sensing

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Digital Communications and Networks 2025年第1期11卷 136-144页

作者： Juan Zhao Haibo Dai Xiaolong Xu Hao Yan Zheng Zhang Chunguo Li School of Network Security Jinling Institute of TechnologyNanjing 211169China School of IoT Nanjing University of Posts and Telecommunications(NJUPT)Nanjing 210003China Jiangsu Key Lab.of Big Data Security and Intelligent Processing NJUPTNanJing 210003China School of Information Science and Engineering Southeast UniversityNanjing 210096China

The secured access is studied in this paper for the network of the image remote *** sensor in this network encounters the information security when uploading information of the images wirelessly from the sensor to the central collection *** order to enhance the sensing quality for the remote uploading,the passive reflection surface technique is *** one eavesdropper that exists nearby this sensor is keeping on accessing the same networks,he may receive the same image from this *** goal in this paper is to improve the SNR of legitimate collection unit while cut down the SNR of the eavesdropper as much as possible by adaptively adjust the uploading power from this sensor to enhance the security of the remote sensing *** order to achieve this goal,the secured energy efficiency performance is theoretically analyzed with respect to the number of the passive reflection elements by calculating the instantaneous performance over the channel fading *** on this theoretical result,the secured access is formulated as a mathematical optimization problem by adjusting the sensor uploading power as the unknown variables with the objective of the energy efficiency maximization while satisfying any required maximum data rate of the eavesdropper ***,the analytical expression is theoretically derived for the optimum uploading *** simulations verify the design approach.

关键词： Image remote sensing Secured access Energy efficiency Sensor transmit power Secured access design

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Text-based person search by non-saliency enhancing and dynamic label smoothing

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Neural Computing and Applications 2024年第21期36卷 13327-13339页

作者： Pang, Yonghua Zhang, Canlong Li, Zhixin Wei, Chunrong Wang, Zhiwen Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Guangxi Key Lab of Multi-source Information Mining and Security Guangxi Normal University Guilin541004 China School of Computer Science and Technology Guangxi University of Science and Technology Liuzhou545006 China

The current text-based person re-identification (re-ID) models tend to learn salient features of image and text, which however is prone to failure in identifying persons with very similar dress, because their image contents with observable but indescribable difference may have identical textual description. To address this problem, we propose a re-ID model based on saliency masking to learn non-salient but highly discriminative features, which can work together with the salient features to provide more robust pedestrian identification. To further improve the performance of the model, a cross-modal projection matching loss with dynamic label smoothing (named CMPM-DS) is proposed to train our model, and our CMPM-DS can adaptively adjust the smoothing degree of the true distribution. We conduct extensive ablation and comparison experiments on two popular re-ID benchmarks to demonstrate the efficiency of our model and loss function, and our model achieves SOTA, improving the existing best R@1 by 0.33% on CUHK-PEDE and 4.45% on RSTPReID. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Person re-identification Cross-modal projection matching Saliency masking label smoothing

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Incorporating metapath interaction on heterogeneous information network for social recommendation

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Frontiers of Computer Science 2024年第1期18卷 33-48页

作者： Yanbin JIANG Huifang MA Xiaohui ZHANG Zhixin LI Liang CHANG College of Computer Science and Engineering Northwest Normal UniversityLanzhou 730070China Guangxi Key Lab of Multi-source Information Mining and Security Guangxi Normal UniversityGuilin 541001China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin 541004China

Heterogeneous information network(HIN)has recently been widely adopted to describe complex graph structure in recommendation systems,proving its effectiveness in modeling complex graph *** existing HIN-based recommendation studies have achieved great success by performing message propagation between connected nodes on the defined metapaths,they have the following major *** works mainly convert heterogeneous graphs into homogeneous graphs via defining metapaths,which are not expressive enough to capture more complicated dependency relationships involved on the ***,the heterogeneous information is more likely to be provided by item attributes while social relations between users are not adequately *** tackle these limitations,we propose a novel social recommendation model MPISR,which models MetaPath Interaction for Social Recommendation on heterogeneous information ***,our model first learns the initial node representation through a pretraining module,and then identifies potential social friends and item relations based on their similarity to construct a unified *** then develop the two-way encoder module with similarity encoder and instance encoder to capture the similarity collaborative signals and relational dependency on different *** experiments on five real datasets demonstrate the effectiveness of our method.

关键词： heterogeneous information network social reco-mmender system metapath interaction attention mechanism

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Lip-Audio Modality Fusion for Deep Forgery Video Detection

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Computers, Materials & Continua 2025年第2期82卷 3499-3515页

作者： Yong Liu Zhiyu Wang Shouling Ji Daofu Gong Lanxin Cheng Ruosi Cheng College of Cyberspace Security Information Engineering UniversityZhengzhou450001China Research Institute of Intelligent Networks Zhejiang LabHangzhou311121China College of Computer Science and Technology Zhejiang UniversityHangzhou310027China Henan Key Laboratory of Cyberspace Situation Awareness Zhengzhou450001China Key Laboratory of Cyberspace Security Ministry of EducationZhengzhou450001China

In response to the problem of traditional methods ignoring audio modality tampering, this study aims to explore an effective deep forgery video detection technique that improves detection precision and reliability by fusing lip images and audio signals. The main method used is lip-audio matching detection technology based on the Siamese neural network, combined with MFCC (Mel Frequency Cepstrum Coefficient) feature extraction of band-pass filters, an improved dual-branch Siamese network structure, and a two-stream network structure design. Firstly, the video stream is preprocessed to extract lip images, and the audio stream is preprocessed to extract MFCC features. Then, these features are processed separately through the two branches of the Siamese network. Finally, the model is trained and optimized through fully connected layers and loss functions. The experimental results show that the testing accuracy of the model in this study on the LRW (Lip Reading in the Wild) dataset reaches 92.3%;the recall rate is 94.3%;the F1 score is 93.3%, significantly better than the results of CNN (Convolutional Neural Networks) and LSTM (Long Short-Term Memory) models. In the validation of multi-resolution image streams, the highest accuracy of dual-resolution image streams reaches 94%. Band-pass filters can effectively improve the signal-to-noise ratio of deep forgery video detection when processing different types of audio signals. The real-time processing performance of the model is also excellent, and it achieves an average score of up to 5 in user research. These data demonstrate that the method proposed in this study can effectively fuse visual and audio information in deep forgery video detection, accurately identify inconsistencies between video and audio, and thus verify the effectiveness of lip-audio modality fusion technology in improving detection performance.

关键词： Deep forgery video detection lip-audio modality fusion mel frequency cepstrum coefficient siamese neural network band-pass filter

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Towards Robust Blockchain Price Oracle: A Study on Human-Centric Node Selection Strategy and Incentive Mechanism

Towards Robust Blockchain Price Oracle: A Study on Human-Cen...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Xian, Youquan Zeng, Xueying Wu, Hao Yang, Danping Wang, Peng Liu, Peng Ministry of Education The Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University Key Lab of Education Blockchain and Intelligent Technology Guilin541004 China

ISBN: (纸本)9781665410205

As a trusted middleware connecting the blockchain and the real world, the blockchain oracle can obtain trusted real-time price information for financial applications such as payment and settlement, and asset valuation on the blockchain. However, the current oracle schemes face the dilemma of security and service quality in the process of node selection, and the implicit interest relationship in financial applications leads to a significant conflict of interest between the task publisher and the executor, which reduces the participation enthusiasm of both parties and system security. Therefore, this paper proposes an anonymous node selection scheme that anonymously selects nodes with high reputations to participate in tasks to ensure the security and service quality of nodes. Then, this paper also details the interest requirements and behavioral motives of all parties in the payment settlement and asset valuation scenarios. Under the hypothesis of rational man, an incentive mechanism based on the Stackelberg game is proposed. It can achieve equilibrium under the pursuit of the revenue of task publishers and executors, thereby ensuring the revenue of all types of users and improving the enthusiasm for participation. Finally, we verify the security of the proposed scheme through security analysis. The experimental results show that the proposed scheme can reduce the variance of obtaining price data by about 55 % while ensuring security, and meeting the revenue of all parties. © 2024 IEEE.

关键词： Decentralized finance

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