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2022 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2022

作者： Zhang, Jucheng Xu, Jiaye Ming, Wanting Sun, Dajun Acoustic Science and Technology Laboratory Harbin Engineering University Harbin150001 China Key Laboratory of Marine Information Acquisition and Security Harbin Engineering University Ministry of Industry and Information Technology Harbin150001 China College of Underwater Acoustic Engineering Harbin Engineering University Harbin150001 China

ISBN: (数字)9781665469722

ISBN: (纸本)9781665469722

data collision or low communication efficiency exist in communication between nodes with the long delay of data transmission in an underwater acoustic network. Therefore, a stable and efficient MAC protocol is a valuable research in underwater information interaction. Among them, the traditional TDMA protocol is widely used because of its simple implementation and effective avoidance of data collision. However, traditional TDMA behaves poor with low channel utilization. To solve this problem, this paper proposes an adaptive TDMA based on mobile nodes without location. Based on the traditional TDMA protocol, the adaptive TDMA integrates long short-term memory (LSTM) network and Q-learning algorithms to predict and adjust the idle waiting slot dynamically before the sending time of each AUV node. Compared with the traditional TDMA and ALOHA, the simulation results show that the the adaptive TDMA based on the LSTM network reduces the end-to-end delay of each node, improves the network throughput, and realizes efficient transmission of underwater information without collision. © 2022 IEEE.

关键词： Reinforcement learning

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Passive Sonar Multi-Target Frequency Feature-Aided data Association Algorithm Based On DPGMM 11

Passive Sonar Multi-Target Frequency Feature-Aided Data Asso...

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11th IEEE International Conference on Computer Science and Network technology, ICCSNT 2023

作者： Wang, Zechong Teng, Tingting Harbin Engineering University Acoustic Science and Technology Laboratory Harbin150001 China Harbin Engineering University Ministry of Industry and Information Technology Key Laboratory of Marine Information Acquisition and Security Harbin150001 China College of Underwater Acoustic Engineering Harbin Engineering University Harbin150001 China

ISBN: (纸本)9798350311594

In passive sonar system, it's always a challenge to accomplish multi-target data association. We propose a remedy for this problem by using a frequency feature-aided data association algorithm to track targets' frequency and bearing information simultaneously. Frequency and bearing information are extracted by Dirichlet Process Gaussian Mixture Model(DPGMM). Simulation results are presented to illustrate the tracking performance of the proposed algorithm in terms of incorrect association, association interrupt times and tracking accuracy aspect. © 2023 IEEE.

关键词： Gaussian distribution

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Ppt4j: Patch Presence Test for Java Binaries 24

Ppt4j: Patch Presence Test for Java Binaries

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44th ACM/IEEE International Conference on Software Engineering, ICSE 2024

作者： Pan, Zhiyuan Hu, Xing Xia, Xin Zhan, Xian Lo, David Yang, Xiaohu Zhejiang University The State Key Laboratory of Blockchain and Data Security Hangzhou China Huawei Software Engineering Application Technology Lab Hangzhou China The Hong Kong Polytechnic University Hong Kong School of Computing and Information Systems Singapore Management University Singapore

ISBN: (纸本)9798400702174

The number of vulnerabilities reported in open source software has increased substantially in recent years. security patches provide the necessary measures to protect software from attacks and vulnerabilities. In practice, it is difficult to identify whether patches have been integrated into software, especially if we only have binary files. Therefore, the ability to test whether a patch is applied to the target binary, a.k.a. patch presence test, is crucial for practitioners. However, it is challenging to obtain accurate semantic information from patches, which could lead to incorrect results. In this paper, we propose a new patch presence test framework named Ppt4j (Patch Presence Test forJava Binaries). Ppt4j is designed for open-source Java libraries. It takes Java binaries (i.e. bytecode files) as input, extracts semantic information from patches, and uses feature-based techniques to identify patch lines in the binaries. To evaluate the effectiveness of our proposed approach Ppt4j, we construct a dataset with binaries that include 110 vulnerabilities. The results show that Ppt4j achieves an F1 score of 98.5% with reasonable efficiency, improving the baseline by 14.2%. Furthermore, we conduct an in-the-wild evaluation of Ppt4j on JetBrains IntelliJ IDEA. The results suggest that a third-party library included in the software is not patched for two CVEs, and we have reported this potential security problem to the vendor. © 2024 ACM.

关键词： Open source software

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Multi-Label Text Classification for Judicial Texts via Dual Graph and Label Feature Fusion 10

Multi-Label Text Classification for Judicial Texts via Dual ...

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10th IEEE Smart World Congress, SWC 2024

作者： Gu, Qiliang Lu, Qin Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China 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 Industrial Network and Information System Security Jinan China

ISBN: (纸本)9798331520861

The legal judgement prediction (LJP) of judicial texts represents a multi-label text classification (MLTC) problem, which in turn involves three distinct tasks: the prediction of charges, legal articles, and terms of penalty. Nevertheless, extant multi-label text classification models tend to eschew the consideration of multiple correlations and semantic information between labels when predicting judicial texts, which may result in the loss of pertinent information. Furthermore, these models do not take full advantage of the local and overall information of the labels in the process of selecting appropriate labels for the text using the multi-head attention mechanism. To address these issues, we propose a new model, BGFLFF, which explores label correlation and semantics among the three tasks by employing Graph Convolutional Network (GCN) and multi-head attention mechanisms. In particular, we propose a Bi-Graph Fusion GCN (BGF-GCN), which fuses the co-occurrence matrix of labels with the cosine similarity matrix, thereby fully exploiting the multiple correlations between labels. Furthermore, prior to embedding the tags, the interpretations of the labels are obtained via Google in order to enhance the semantic information between the labels. To further reinforce the interconnections between labels and text and more effectively comprehend the interrelationships between the three tasks, we propose a Multi-Head Label Feature Fusion Attentional Mechanism (MH-LFFAtt), which assigns distinct weights to the pertinent label information in the text by fusing the local and overall features of the labels. The experimental results demonstrate that the F1 score exhibits an improvement of up to 2. 2 8 % and a minimum of 1. 2 9 % across the various tasks of the two datasets. This evidence substantiates the assertion that BGFLFF outperforms the existing baseline model. © 2024 IEEE.

关键词： Semantics

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What's the Real: A Novel Design Philosophy for Robust AI-Synthesized Voice Detection 24

What's the Real: A Novel Design Philosophy for Robust AI-Syn...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Hai, Xuan Liu, Xin Tan, Yuan Liu, Gang Li, Song Niu, Weina Zhou, Rui Zhou, Xiaokang School of Information Science and Engineering Lanzhou University Lanzhou China The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Business Data Science Kansai University Osaka Japan

ISBN: (纸本)9798400706868

Voice is one of the most widely used media for information transmission in human society. While high-quality synthetic voices are extensively utilized in various applications, they pose significant risks to content security and trust building. Numerous studies have concentrated on AI-synthesized voice detection to mitigate these risks, with many claiming to achieve promising performance. However, recent research has demonstrated that fake voice detectors suffer from serious overfitting to speaker-irrelative features (SiFs) and cannot be used in real-world scenarios. In this paper, we analyze the limitations of existing fake voice detectors and propose a new design philosophy, guiding the detection model to prioritize learning human voice features rather than the difference between the human voice and the synthetic voice. Based on this philosophy, we propose a novel AI-synthesized voice detection framework named SiFSafer, which uses pre-trained speech representation models to enhance the learning of feature distribution in human voices and the adapter fine-tuning to optimize the performance. The evaluation shows that the average EERs of existing fake voice detectors in the ASVspoof datasets can exceed 20% if the SiFs like silence segments are removed, while SiFSafer achieves an EER of less than 8%, indicating that SiFSafer is robust to SiFs and strongly resistant to existing attacks. © 2024 ACM.

关键词： Fake detection

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A multiple sensitive attributes data publishing method with guaranteed information utility

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CAAI Transactions on Intelligence technology 2023年第2期8卷 288-296页

作者： Haibin Zhu Tong Yi Songtao Shang Minyong Shi Zhucheng Li Wenqian Shang Department of Computer Science and Mathematics Nipissing UniversityNorth BayOntarioCanada Key Lab of Education Blockchain and Intelligent Technology Ministry of EducationGuangxi Normal UniversityGuilinChina Guangxi Key Lab of Multi‐Source Information Mining and Security Guangxi Normal UniversityGuilinChina School of Computer and Communication Engineering Zhengzhou University of Light IndustryZhengzhouChina School of Computer Science Communication University of ChinaBeijingChina Business College Beijing Union UniversityBeijingChina

data publishing methods can provide available information for analysis while preserving *** multiple sensitive attributes data publishing,which preserves the relationship between sensitive attributes,may keep many records from being grouped and bring in a high record suppression *** category of multiple sensitive attributes data publishing,which reduces the possibility of record suppression by breaking the relationship between sensitive attributes,cannot provide the sensitive attributes association for ***,the existing multiple sensitive attributes data publishing fails to fully account for the comprehensive information *** acquire a guaranteed information utility,this article defines comprehensive information loss that considers both the suppression of records and the relationship between sensitive attributes.A heuristic method is leveraged to discover the optimal anonymity scheme that has the lowest comprehensive information *** experimental results verify the practice of the proposed data publishing method with multiple sensitive *** proposed method can guarantee information utility when compared with previous ones.

关键词： data analysis data privacy security of data

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Differentially Private Generative Model with Ratio-Based Gradient Clipping 4th

Differentially Private Generative Model with Ratio-Based Gr...

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

作者： Lin, Jianchen Yao, Yanqing State Key Laboratory of Software Development Environment Beihang University Beijing100191 China State Key Laboratory of Cryptology Beijing100878 China Key Laboratory of Aerospace Network Security Ministry of Industry and Information Technology School of Cyber Science and Technology Beihang University Beijing100191 China State Grid Fujian Fuzhou350100 China

ISBN: (纸本)9783031200953

As a branch of deep learning, conditional Generative Adversarial Net (cGAN) has achieved remarkable results in target-oriented image generation, but the process of training the model lacks privacy guarantee. Differential Privacy (DP) is a privacy-preserving mechanism that could be utilized in the Stochastic Gradient Descent (SGD) of deep learning to maintain the confidentiality of training data. However, the quality and diversity of differentially private conditional image synthesis remain large room for improvement because traditional mechanisms with thick granularities and rigid clipping bounds in Differentially Private SGD (DPSGD) could lead to huge performance loss. To tackle this, we propose three algorithms with different clipping granularities and clipping bound distributions, and investigate the effects on enhancing conditional image generation under Differentially Private Projection cGAN (DP-PcGAN). We compare and analyze the performance of our strategies with methods concerned in terms of seven metrics, finding ratio-based gradient clipping highly effective and robust in training DP-PcGAN for generating visually promising images. Specifically, the Per-layer RGC surpasses previous methods under all evaluation metrics utilizing the second least privacy expense for MNIST dataset, while Per-unit RGC attains encouraging outcomes with a moderate converging speed under FashionMNIST dataset. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Stochastic systems

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Improved Density-Based data Association Method Under Azimuth Rate Constraint

Improved Density-Based Data Association Method Under Azimuth...

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2024 OES China Ocean Acoustics, COA 2024

作者： Qi, Bin Hui, Chenxin Hao, Yu Li, Xiang Mathiopoulos, P. Takis Harbin Engineering University National Key Laboratory of Underwater Acoustic Technology Harbin150001 China Ministry of Industry and Information Technology Key Laboratory of Marine Information Acquisition and Security Harbin150001 China College of Underwater Acoustic Engineering Harbin Engineering University Harbin150001 China National and Kapodistrian University of Athens Department of Informatics and Telecommunications Greece

ISBN: (纸本)9798350340297

Passive sonar has been extensively researched in recent years. However, bearing-only tracking faces significant challenges because passive detection only provides target information. Traditional algorithms estimate target states using Rao-Blackwellized Monte Carlo data Association and form target trajectories using clustering and label management algorithms. However, the tracking effectiveness of this method depends on the results of the clustering algorithm, which leads to trajectory discontinuity in scenarios with target trajectory crossings. This study addresses the problem of trajectory crossing in passive scenarios by proposing an improved clustering method and label management logic. The experimental results confirmed that the proposed algorithm in this scenario can accurately estimate the number of targets and achieve robust target tracking. © 2024 IEEE.

关键词： Trajectories

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Cross-modality information Check for Detecting Jailbreaking in Multimodal Large Language Models

arXiv

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arXiv 2024年

作者： Xu, Yue Qi, Xiuyuan Qin, Zhan Wang, Wenjie School of Information Science and Technology ShanghaiTech University China The State Key Laboratory of Blockchain and Data Security Zhejiang University China

Multimodal Large Language Models (MLLMs) extend the capacity of LLMs to understand multimodal information comprehensively, achieving remarkable performance in many vision-centric tasks. Despite that, recent studies have shown that these models are susceptible to jailbreak attacks, which refer to an exploitative technique where malicious users can break the safety alignment of the target model and generate misleading and harmful answers. This potential threat is caused by both the inherent vulnerabilities of LLM and the larger attack scope introduced by vision input. To enhance the security of MLLMs against jailbreak attacks, researchers have developed various defense techniques. However, these methods either require modifications to the model’s internal structure or demand significant computational resources during the inference phase. Multimodal information is a double-edged sword. While it increases the risk of attacks, it also provides additional data that can enhance safeguards. Inspired by this, we propose Cross-modality information DEtectoR (CIDER), a plug-and-play jailbreaking detector designed to identify maliciously perturbed image inputs, utilizing the cross-modal similarity between harmful queries and adversarial images. CIDER is independent of the target MLLMs and requires less computation cost. Extensive experimental results demonstrate the effectiveness and efficiency of CIDER, as well as its transferability to both white-box and black-box MLLMs. The resource is available at https://***/PandragonXIII/CIDER. Copyright © 2024, The Authors. All rights reserved.

关键词： Wine

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Semisupervised Encrypted Traffic Identification Based on Auxiliary Classification Generative Adversarial Network

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Computer Systems Science & Engineering 2021年第12期39卷 373-390页

作者： Jiaming Mao Mingming Zhang Mu Chen Lu Chen Fei Xia Lei Fan ZiXuan Wang Wenbing Zhao State Grid Jiangsu Electric Power Co. Ltd.Information and Telecommunication BranchNanJing210003China Institute of Information and Communication Global Energy Interconnection Research InstituteState Grid Key Laboratory of Information and Network SecurityNanjing210003China Engineering Research Center of Post Big Data Technology and Application of Jiangsu Province Research and Development Center of Post Industry Technology of the State Posts Bureau(Internet of Things Technology)Broadband Wireless Communication Technology Engineering Research Center of the Ministry of EducationNanjing University of Posts and TelecommunicationsNanjing210003China Department of Electrical Engineering and Computer Science Cleveland State UniversityCleveland44115USA

The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased net-work traffic *** the past few decades,network traffic identification has been a research hotspot in the field of network management and security ***,as more network services use encryption technology,network traffic identification faces many *** classic machine learning methods can solve many problems that cannot be solved by port-and payload-based methods,manually extract features that are frequently updated is time-consuming and *** learning has good automatic feature learning capabilities and is an ideal method for network traffic identification,particularly encrypted traffic identification;Existing recognition methods based on deep learning primarily use supervised learning methods and rely on many labeled ***,in real scenarios,labeled samples are often difficult to *** paper adjusts the structure of the auxiliary classification generation adversarial network(ACGAN)so that it can use unlabeled samples for training,and use the wasserstein distance instead of the original cross entropy as the loss function to achieve semisupervised *** results show that the identification accuracy of ISCX and USTC data sets using the proposed method yields markedly better performance when the number of labeled samples is small compared to that of convolutional neural network(CNN)based classifier.

关键词： Encrypted traffic recognition deep learning generative adversarial network traffic classification semisupervised learning

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