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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Changnan Jiang Chunhe Xia Chen Chen Huacheng Li Tianbo Wang Xiaojian Li Beijing Key Lab. of Network Technology Beihang University Beijing China Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing Guangxi Normal University Guilin China School of Cyber Science and Technology Beihang University Beijing China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China College of Computer Science and Information Technology Guangxi Normal University Guilin China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

In the traditional centralized Android malware classification framework, privacy concerns arise as it requires collecting users’ app samples containing sensitive information directly. To address this problem, new classification frameworks based on Federated Learning (FL) have emerged for privacy preservation. However, research shows that these frameworks still face risks of indirect information leakage due to adversary inference. Unfortunately, existing research lacks an effective assessment of the extent and location of this leakage risk. To bridge the gap, we propose the FedDLM, which provides a fine-grained assessment of the risk of sensitive information leakage in an FL-based Android malware classifier. FedDLM estimates attackers’ theoretical maximum inference ability from the information theory perspective to gauge the degree of leakage risk in the classifier effectively. It precisely identifies critical positions in the shared gradient where the leakage risk exists by utilizing characteristics of class activation in classifiers. Through extensive experiments on the Androzoo dataset, FedDLM demonstrates its superior effectiveness and precision compared to baseline methods in evaluating the risk of sensitive information leakage. The evaluation results provide valuable insights into information leakage problems in classifiers and targeted privacy protection methods.

关键词： Meters Privacy Federated learning information leakage Malware Security Risk management

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Quantitative Measurement of Coronary Artery Stenosis in CCTA Images Using a 2D Parametric Intensity Model

Quantitative Measurement of Coronary Artery Stenosis in CCTA...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Guanyu YANG Xinglou ZHAO Lijun TANG Huzhong SHU Christine TOUMOULIN Lab of Image Science and Technology Key Laboratory of Computer Network and Information Integration (Southeast University) Dept. of Radiology the First Affiliated Hospital of Nanjing Medical University INSERM-U1099 LTSI Universite de Rennes 1

ISBN: (纸本)9781424479276

In this paper, we propose an approach based on 2D vessel model to segment the vessel lumen in three-dimensional coronary computed tomographic angiography (CCTA) images. The 2D parametric intensity model is introduced first to simulate the intensity distribution of vessel lumen with different size in the longitudinal images. Then the Levenberg-Marquardt method is applied to fit the model within a series of region-of interests defined in the longitudinal image. The estimated parameters of the model are employed to define the boundary points of vessel lumen. The detected boundary points of vessel lumen in six longitudinal images are transformed to the cross-sectional planes in order to calculate the degree of stenosis according to the luminal areas. Our proposed method was evaluated in ten CCTA images with ten reported non-calcified stenosis. The degree of each stenosis was computed according to the luminal area and compared with the standard reference given by radiologists. Experimental results show that our method can estimate the degree of stenosis with a high accuracy.

关键词： Image segmentation Arteries Fitting Solid modeling Three-dimensional displays Computational modeling Accuracy

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Event-based Video Frame Interpolation with Edge Guided Motion Refinement

arXiv

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arXiv 2024年

作者： Liu, Yuhan Deng, Yongjian Chen, Hao Xie, Bochen Li, Youfu Yang, Zhen The College of Computer Science Beijing University of Technology Beijing100124 China Department of Mechanical Engineering City University of Hong Kong Kowloon Hong Kong Hong Kong Key Lab of Computer Network and Information Integration Southeast University Ministry of Education Nanjing211189 China

Video frame interpolation, the process of synthesizing intermediate frames between sequential video frames, has made remarkable progress with the use of event cameras. These sensors, with microsecond-level temporal resolution, fill information gaps between frames by providing precise motion cues. However, contemporary Event-Based Video Frame Interpolation (E-VFI) techniques often neglect the fact that event data primarily supply high-confidence features at scene edges during multi-modal feature fusion, thereby diminishing the role of event signals in optical flow (OF) estimation and warping refinement. To address this overlooked aspect, we introduce an end-to-end E-VFI learning method (referred to as EGMR) to efficiently utilize edge features from event signals for motion flow and warping enhancement. Our method incorporates an Edge Guided Attentive (EGA) module, which rectifies estimated video motion through attentive aggregation based on the local correlation of multi-modal features in a coarse-to-fine strategy. Moreover, given that event data can provide accurate visual references at scene edges between consecutive frames, we introduce a learned visibility map derived from event data to adaptively mitigate the occlusion problem in the warping refinement process. Extensive experiments on both synthetic and real datasets show the effectiveness of the proposed approach, demonstrating its potential for higher quality video frame interpolation. Copyright © 2024, The Authors. All rights reserved.

关键词： Cameras

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Deep Video Cascade Clustering from Video Structures

SSRN

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SSRN 2024年

作者： Wang, Zihua Zhu, Hongyuan Mi, Siya Zhang, Yu Geng, Xin School of Computer Science and Engineering The Key Lab of Computer Network and Information Integration Ministry of Education Southeast University Nanjing211189 China A*STAR Singapore School of Cyber Science and Engineering Southeast University Nanjing211189 China Purple Mountain Laboratories Nanjing211189 China

Video clustering is a critical unsupervised learning task where, unlike in supervised video classification, category lab.ls are not *** main challenge lies in learning an effective video representation without annotations to group similar *** this paper, we introduce a novel unsupervised video learning paradigm called Deep Video Cascade Clustering ($\text{DVC}^2$).Unlike existing image-based clustering methods, $\text{DVC}^2$ first learns an initial video representation through frame clustering, which serves as guidance. Subsequently, the video clustering results are aligned with both the long-term and short-term video structures and their nearest *** conduct a comprehensive evaluation of $\text{DVC}^2$ on benchmark video datasets, including UCF101 and Kinetic-400, and achieve state-of-the-art video clustering results. Additionally, we evaluate the performance of combining self-supervised learning with K-means *** findings validate the effectiveness of $\text{DVC}^2$ in unsupervised video clustering scenarios. © 2024, The Authors. All rights reserved.

关键词： Unsupervised learning

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Image-Text Retrieval with Intra-Modal Soft lab.ls

SSRN

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SSRN 2022年

作者： Zhang, Yu Liu, Haoliang Xiong, Wei Mi, Siya Bian, Yali School of Computer Science and Engineering The Key Lab of Computer Network and Information Integration Ministry of Education Southeast University Nanjing211189 China School of Cyber Science and Engineering Southeast University Nanjing211189 China Purple Mountain Laboratories Nanjing China Intel Labs United States

Image-text retrieval attempts to find relevant images from a huge corpus of images given a textual description, and vice versa. Existing works usually denote the negative/positive image-text pair with a binary lab.l. They assume that a text is only relevant to the paired images and irrelevant to other images in the corpus. However, using the binary lab.ls cannot reveal the precise similarity between an image and a text. In this paper, we learn the model using soft lab.ls, where each image-text pair is associated with a specific soft lab.l. We generate the Intra-modal Soft lab.l (ISL) between the cross-modal (image, text) pair by the similarity between the intra-modal (image caption, text) pair. Systematic experiments on public benchmarks, including MSCOCO and Flickr30K, demonstrate the effectiveness of the proposed method, which achieves a recall@1 improvement of approximately 1%-3% compared to the baseline method. © 2022, The Authors. All rights reserved.

关键词： information retrieval

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HL-DPoS: An Enhanced Anti-Long-Range Attack DPoS Algorithm

arXiv

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arXiv 2023年

作者： Li, Yang Xia, Chunhe Li, Chunyan Zhao, Yuan Chen, Chen Wang, Tianbo School of Computer Science and Engineering Beihang University Beijing China Beijing Key Lab. of Network Technology Beihang University Beijing China Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing Guangxi Normal University Guilin China School of Cyber Science and Technology Beihang University Beijing China

The consensus algorithm is crucial in blockchain for ensuring the validity and security of transactions across the decentralized network. However, achieving consensus among nodes and packaging blocks in blockchain networks is a complex task that requires efficient and secure consensus algorithms. The DPoS consensus algorithm has emerged as a popular choice due to its fast transaction processing and high throughput. Despite these advantages, the algorithm still suffers from weaknesses such as centralization and vulnerability to long-range attacks, which can compromise the integrity of the blockchain network. To combat these problems, we developed an Enhanced Anti-Long-Range Attack DPoS algorithm (HL-DPoS). First, we split nodes into pieces to reduce centralization issues while giving witness nodes the power to report and benefit from malicious node's reports, maintaining high efficiency and high security. Second, we propose a validation method in HL-DPoS that compares consensuses transactions with the longest chain to detect long-range attacks. Algorithm analysis and simulation experiment results demonstrate that our HL-DPoS consensus algorithm improves security while achieving better consensus performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Blockchain

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Research on network malicious code dendritic cell immune algorithm based on fuzzy weighted support vector machine

Research on network malicious code dendritic cell immune alg...

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6th China Conference of Wireless Sensor networks, CWSN 2012

作者： Li, Peng Wang, Ruchuan Zhou, Yanting Dai, Qiuyu College of Computer Nanjing University of Posts and Telecommunications Nanjing Jiangsu 210003 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing Jiangsu 210003 China Key Lab. of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Ministry of Education Jiangsu Province Nanjing Jiangsu 210003 China College of Telecommunications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing Jiangsu 210003 China

ISBN: (纸本)9783642362514

According to the significant impact on the accuracy rate of detection of current immune algorithms brought by incorrect classification of signal, it proposes network malicious code dendritic cell immune algorithm based on fuzzy weighted support vector machine. It summarizes and compares the pros and cons of the four methods, which are basic artificial intelligence immune algorithm, detection of unknown viruses based on immunity theory, malicious code immunity based on cryptography and automated intrusion response based on danger theory. It elab.rates the process of the algorithm of the proposed immune algorithm, discusses a variety of input and output signals, gives the samples of the actual input signals, applies the coefficient of variation method to determine the values of the weights so as to enhance the discrimination ability of signal processing results, describes the principle and algorithm steps of fuzzy weighted support vector machine clustering method within immune algorithm proposed. By comparing the fuzzy aggregation before and after the immunization program experiments, it draws the conclusion that the proposed immune algorithm can optimize the input signal, fuzzy clustering the signal and the antigen, so as to bring down the number of immunization strategies and reduce the immune response time, as a result it improve the efficiency and performance of the immune system. © 2013 Springer-Verlag.

关键词： Cells

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Few-shot Encrypted Malicious Traffic Classification via Hierarchical Semantics and Adaptive Prototype Learning

Few-shot Encrypted Malicious Traffic Classification via Hier...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Yuan Zhao Chunhe Xia Tianbo Wang Mengyao Liu Yang Li Beijing Key Lab. of Network Technology Beihang University Beijing China Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing Guangxi Normal University Guilin China School of Cyber Science and Technology Beihang University Beijing China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

While encrypted traffic improves security, it is also used by attackers to hide the transmission content to evade detection. Currently, traffic side-channel features combined with Deep Learning (DL) are widely used for malicious traffic classification, but traditional DL-based methods require large training samples and struggle with new threats. Prototypical networks in meta-learning have been effective in few-shot malicious traffic classification. However, existing methods face challenges such as "overlooking hierarchical traffic dependencies" and "bias in class prototype generation". The former means that the existing methods lack the representation design based on the hierarchical structure of traffic, resulting in insufficient feature extraction, and the latter indicates that the existing methods struggle to capture the diverse distribution of traffic features, resulting in unstable classification performance. To address the above problems, this paper proposes a few-shot encrypted malicious traffic classification method based on Hierarchical Semantics and Adaptive Prototype Learning network (HANet). First, network traffic’s fine-grained features are represented in a multi-level matrix, with a hierarchical network structure designed to extract features comprehensively. Then, class prototypes are dynamically generated using a neighborhood partitioning method to balance simple and complex traffic feature distributions, enhancing generalization. Experiments on the CICandMal2017 dataset show that HANet offers significant performance over other few-shot malicious traffic classification methods. HANet has achieved a classification accuracy of more than 80% with only 5, 10, and 15 lab.led traffic samples, realizing effective detection of few-shot encrypted malicious traffic.

关键词： Training Metalearning Privacy Semantics Prototypes Telecommunication traffic Feature extraction Loss measurement Cryptography Ransomware

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MutexMatch: Semi-Supervised Learning with Mutex-Based Consistency Regularization

arXiv

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arXiv 2022年

作者： Duan, Yue Zhao, Zhen Qi, Lei Wang, Lei Zhou, Luping Shi, Yinghuan Gao, Yang The National Key Laboratory for Novel Software Technology The National Institute of Healthcare Data Science Nanjing University Nanjing210023 China The School of Electrical and Information Engineering The University of Sydney SydneyNSW2006 Australia The School of Computer Science and Engineering The Key Lab of Computer Network and Information Integration Ministry of Education Southeast University Nanjing211189 China The School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia

The core issue in semi-supervised learning (SSL) lies in how to effectively leverage unlab.led data, whereas most existing methods tend to put a great emphasis on the utilization of high-confidence samples yet seldom fully explore the usage of low-confidence samples. In this paper, we aim to utilize low-confidence samples in a novel way with our proposed mutex-based consistency regularization, namely MutexMatch. Specifically, the high-confidence samples are required to exactly predict "what it is" by conventional True-Positive Classifier, while low-confidence samples are employed to achieve a simpler goal — to predict with ease "what it is not" by True-Negative Classifier. In this sense, we not only mitigate the pseudo-lab.ling errors but also make full use of the low-confidence unlab.led data by consistency of dissimilarity degree. MutexMatch achieves superior performance on multiple benchmark datasets, i.e., CIFAR-10, CIFAR-100, SVHN, STL-10, mini-ImageNet and Tiny-ImageNet. More importantly, our method further shows superiority when the amount of lab.led data is scarce, e.g., 92.23% accuracy with only 20 lab.led data on CIFAR-10. Our code and model weights have been released at https://***/NJUyued/MutexMatch4SSL. Copyright © 2022, The Authors. All rights reserved.

关键词： Benchmarking

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FedDADP: A Privacy-Risk-Adaptive Differential Privacy Protection Method for Federated Android Malware Classifier

FedDADP: A Privacy-Risk-Adaptive Differential Privacy Protec...

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International Joint Conference on Neural networks (IJCNN)

作者： Changnan Jiang Chunhe Xia Mengyao Liu Rui Fang Pengfei Li Huacheng Li Tianbo Wang Beijing Key Lab. of Network Technology Beihang University Beijing China Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing Guangxi Normal University Guilin China School of Cyber Science and Technology Beihang University Beijing China SNationa Innovation Center of Intelligent and Connected Vehicles Beijing China Shanghai Key Laboratory of Computer Software Evaluating and Testing China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The federated Android malware classifier has attracted much attention owing to its advantages of privacy protection and multi-party joint modeling. However, the research indicates that the gradient transmitted within the federated classifier still encodes the user's sensitive information, exposing it to indirect privacy inference threats from curious servers. Differential privacy is a recognized and effective way to address this privacy breach threat by adding noise to the user's model parameters to limit the attacker's inference of sensitive information. However, the protection effect of existing differential privacy methods is at the cost of significantly reducing the model's classification accuracy, and it cannot be reasonably balanced. To address this challenge, we propose a privacy protection method, FedDADP. FedDADP performs adaptive, lightweight privacy configuration in its training time dimension and model space dimension according to the privacy risk distribution law in the federated Android malware classifier to protect users' privacy while maintaining the model's utility. Numerous experiments on the Androzoo dataset and multiple baseline classifiers show that FedDADP protects users' sensitive information better (7% more effectiveness against adversaries' inference) than baseline differential privacy methods and achieves better model utility (classification accuracy improves by about 8%) with the same privacy budget.

关键词： Training Privacy Adaptation models Differential privacy Accuracy Privacy breach Malware

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