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High-speed encrypted traffic classification by using payload features

Digital communications and Networks 2025年第2期11卷 412-423页

作者： Xinge Yan Liukun He Yifan Xu Jiuxin Cao Liangmin Wang Guyang Xie School of Cyber Science and Engineering Southeast University Nanjing Huafei Data Technology Co. Ltd. School of Computer Science and Communication Engineering Jiangsu University

Traffic encryption techniques facilitate cyberattackers to hide their presence and activities. Traffic classification is an important method to prevent network threats. However, due to the tremendous traffic volume and limitations of computing, most existing traffic classification techniques are inapplicable to the high-speed network environment. In this paper, we propose a High-speed Encrypted Traffic Classification(HETC) method containing two stages. First, to efficiently detect whether traffic is encrypted, HETC focuses on randomly sampled short flows and extracts aggregation entropies with chi-square test features to measure the different patterns of the byte composition and distribution between encrypted and unencrypted flows. Second, HETC introduces binary features upon the previous features and performs fine-grained traffic classification by combining these payload features with a Random Forest model. The experimental results show that HETC can achieve a 94% F-measure in detecting encrypted flows and a 85%–93% F-measure in classifying fine-grained flows for a 1-KB flow-length dataset, outperforming the state-of-the-art comparison methods. Meanwhile, HETC does not need to wait for the end of the flow and can extract mass computing features. The average time for HETC to process each flow is only 2 or 16 ms, which is lower than the flow duration in most cases, making it a good candidate for high-speed traffic classification.

关键词： Traffic classification Flow analysis Information entropy Machine learning Randomness test

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Synergistic Spotting and Recognition of Micro-Expression via Temporal State Transition

Synergistic Spotting and Recognition of Micro-Expression via...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zou, Bochao Guo, Zizheng Qin, Wenfeng Li, Xin Wang, Kangsheng Ma, Huimin School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Intelligence Science and Technology University of Science and Technology Beijing Beijing China

ISBN: (纸本)9798350368741

Micro-expressions are involuntary facial movements that cannot be consciously controlled, conveying subtle cues with substantial real-world applications. The analysis of micro-expressions generally involves two main tasks: spotting micro-expression intervals in long videos and recognizing the emotions associated with these intervals. Previous deep-learning methods have primarily relied on classification networks utilizing sliding windows. However, fixed window sizes and window-level hard classification introduce numerous constraints. Additionally, these methods have not fully exploited the potential of complementary pathways for spotting and recognition. In this paper, we present a novel temporal state transition architecture grounded in the state space model, which replaces conventional window-level classification with video-level regression. Furthermore, by leveraging the inherent connections between spotting and recognition tasks, we propose a synergistic strategy that enhances overall analysis performance. Extensive experiments demonstrate that our method achieves state-of-the-art performance. The codes are available at https://***/zizheng-guo/ME-TST. © 2025 IEEE.

关键词： Long videos Micro-expression analysis State space model Synergistic spotting and recognition

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Selective sampling and temporal positional encoding for monocular video based 3D human pose and shape estimation 5

Selective sampling and temporal positional encoding for mono...

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5th International Conference on Signal Processing and computer science, SPCS 2024

作者： Hou, Yupeng Wen, Han Zeng, Guangping School of Computer & Communication Engineering University of Science and Technology Beijing Beijing100083 China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

This study introduces a novel framework for video-based 3D human pose and shape estimation, termed Selective sampling and Temporal Positional Encoding (STPE). Our method leverages selective sampling and advanced positional encoding to tackle the temporal complexities of video data and the high cost and scarcity of annotated datasets. Inspired by the Masked Autoencoder (MAE), our approach adopts a selective sampling strategy that efficiently captures the essential dynamics of human motion from partial views, significantly reducing reliance on continuous frames. The framework incorporates Rotary Position Embedding (RoPE), using rotational angles to simplify positional encoding. This innovation decreases model complexity and boosts learning effectiveness. We also introduce randomized index positions during training, introducing variability and enhancing generalization across various datasets and motion patterns. Our model, validated on standard datasets like 3DPW, MPI-INF-3DHP, and Human3.6M, shows enhanced performance in accurate and robust 3D pose and shape capture compared to existing methods. Our results demonstrate that strategic frame sampling and sophisticated positional encoding can significantly improve accuracy and robustness of video-based pose estimation systems. © 2025 SPIE.

关键词： Video recording

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Adaptive Dispersion Estimation for Linearized Electronic Dispersion Compensation in IM-DD Systems

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China communications 2025年第4期22卷 55-64页

作者： Huo Jiahao Tao Jianlong Zhang Xiaoying Zhu Jin Qin Peng Wei Huangfu School of Computer and Communication Engineering University of Science and TechnologyBeijing 100083China

An adaptive dispersion estimation(ADE)is proposed to compensate dispersion and estimate the transfer function of the fiber channel with GerchbergSaxton(G-S)algorithm,using the stochastic gradient descent(SGD)method in the intensity-modulation and direct-detection(IM-DD)system,improving the tolerance of the algorithm to chromatic dispersion(CD).In order to address the divergence arising from the perturbation in the amplitude of the received signal caused by the filtering effect of the non-ideal channels,a channel-compensation equalizer(CCE)derived from the back-to-back(BTB)scenario is employed at the transmitter to make the amplitude of the received signal depicting the CD effect more *** simulation results demonstrate the essentiality of CCE for the convergence and performance improvement of the G-S *** show that it supports 112Gb/s four-level pulse amplitude modulation(PAM4)over 100 km standard single-mode fiber(SSMF)transmission under the 7%forward error correction(FEC)threshold of ***,ADE improves the tolerance to wavelength drift from about 4 nm to 42 nm,and there is a better tolerance for fiber distance perturbation,indicating the G-S algorithm and its derived algorithms with the ADE scheme exhibit superior robustness to the perturbation in the system.

关键词： chromatic dispersion digital signal process intensity modulation and direct detection

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A Lightweight Multiscale Feature Fusion Network for Solar Cell Defect Detection

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computers, Materials & Continua 2025年第1期82卷 521-542页

作者： Xiaoyun Chen Lanyao Zhang Xiaoling Chen Yigang Cen Linna Zhang Fugui Zhang School of Mechanical Engineering Guizhou UniversityGuiyang550025China School of Computer Science and Technology Beijing Jiaotong UniversityBeijing100044China

Solar cell defect detection is crucial for quality inspection in photovoltaic power generation *** the production process,defect samples occur infrequently and exhibit random shapes and sizes,which makes it challenging to collect defective ***,the complex surface background of polysilicon cell wafers complicates the accurate identification and localization of defective *** paper proposes a novel Lightweight Multiscale Feature Fusion network(LMFF)to address these *** network comprises a feature extraction network,a multi-scale feature fusion module(MFF),and a segmentation ***,a feature extraction network is proposed to obtain multi-scale feature outputs,and a multi-scale feature fusion module(MFF)is used to fuse multi-scale feature information *** order to capture finer-grained multi-scale information from the fusion features,we propose a multi-scale attention module(MSA)in the segmentation network to enhance the network’s ability for small target ***,depthwise separable convolutions are introduced to construct depthwise separable residual blocks(DSR)to reduce the model’s parameter ***,to validate the proposed method’s defect segmentation and localization performance,we constructed three solar cell defect detection datasets:SolarCells,SolarCells-S,and *** and SolarCells-S are monocrystalline silicon datasets,and PVEL-S is a polycrystalline silicon *** results show that the IOU of our method on these three datasets can reach 68.5%,51.0%,and 92.7%,respectively,and the F1-Score can reach 81.3%,67.5%,and 96.2%,respectively,which surpasses other commonly usedmethods and verifies the effectiveness of our LMFF network.

关键词： Defect segmentation multi-scale feature fusion multi-scale attention depthwise separable residual block

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Key Agreement and Management Scheme Based on Blockchain for 5G-Enabled Vehicular Networks

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China communications 2025年第3期22卷 270-287页

作者： Wang Zhihua Wang Shuaibo Wang Haofan Li Jiaze Yao Yizhe Wang Yongjian Yang Xiaolong School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China School of Cyber Science and Engineering Zhengzhou UniversityZhengzhou 449001China Lab of National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing 100029China

5G technology has endowed mobile communication terminals with features such as ultrawideband access,low latency,and high reliability transmission,which can complete the network access and interconnection of a large number of devices,thus realizing richer application scenarios and constructing 5G-enabled vehicular ***,due to the vulnerability of wireless communication,vehicle privacy and communication security have become the key problems to be solved in vehicular ***,the large-scale communication in the vehicular networks also makes the higher communication efficiency an inevitable *** order to achieve efficient and secure communication while protecting vehicle privacy,this paper proposes a lightweight key agreement and key update scheme for 5G vehicular networks based on ***,the key agreement is accomplished using certificateless public key cryptography,and based on the aggregate signature and the cooperation between the vehicle and the trusted authority,an efficient key updating method is proposed,which reduces the overhead and protects the privacy of the vehicle while ensuring the communication ***,by introducing blockchain and using smart contracts to load the vehicle public key table for key management,this meets the requirements of vehicle traceability and can dynamically track and revoke misbehaving ***,the formal security proof under the eck security model and the informal security analysis is conducted,it turns out that our scheme is more secure than other authentication schemes in the vehicular *** analysis shows that our scheme has lower overhead than existing schemes in terms of communication and computation.

关键词： blockchain certificateless public key cryptography 5G vehicular networks key agreement key management

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PMMTalk: Speech-Driven 3D Facial Animation From Complementary Pseudo Multi-Modal Features

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IEEE Transactions on Multimedia 2025年 27卷 2570-2581页

作者： Han, Tianshun Gui, Shengnan Huang, Yiqing Li, Baihui Liu, Lijian Zhou, Benjia Jiang, Ning Lu, Quan Zhi, Ruicong Liang, Yanyan Zhang, Du Wan, Jun Macau University of Science and Technology School of Computer Science and Engineering Faculty of Innovation Engineering 999078 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Mashang Consumer Finance Company Ltd. Chongqing400000 China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083 China

Speech-driven 3D facial animation has improved a lot recently while most related works only utilize acoustic modality and neglect the influence of visual and textual cues, leading to unsatisfactory results in terms of precision and coherence. We argue that visual and textual cues are not trivial information. Therefore, we present a novel framework, namely PMMTalk, using complementary Pseudo Multi-Modal features for improving the accuracy of facial animation. The framework entails three modules: PMMTalk encoder, cross-modal alignment module, and PMMTalk decoder. Specifically, the PMMTalk encoder employs the off-the-shelf talking head generation architecture and speech recognition technology to extract visual and textual information from speech, respectively. Following this, the cross-modal alignment module aligns the audio-image-text features at temporal and semantic levels. Subsequently, the PMMTalk decoder is employed to predict lip-syncing facial blendshape coefficients. Contrary to prior methods, PMMTalk only requires an additional random reference face image but yields more accurate results. Additionally, it is artist-friendly as it seamlessly integrates into standard animation production workflows by introducing facial blendshape coefficients. Finally, given the scarcity of 3D talking face datasets, we introduce a large-scale 3D Chinese Audio-Visual Facial Animation (3D-CAVFA) dataset. Extensive experiments and user studies show that our approach outperforms the state of the art. Codes and datasets are available at PMMTalk. © 1999-2012 IEEE.

关键词： Animation

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An Automated Approach to Discovering Software Refactorings by Comparing Successive Versions

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IEEE Transactions on Software engineering 2025年第5期51卷 1358-1380页

作者： Liu, Bo Liu, Hui Niu, Nan Zhang, Yuxia Li, Guangjie Jiang, He Jiang, Yanjie University of Cincinnati Department of Electrical Engineering and Computer Science CincinnatiOH United States National Innovation Institute of Defense Technology Beijing China School of Software Dalian University of Technology Dalian China Beijing Institute of Technology School of Computer Science and Technology Beijing China Peking University School of Computer Science Beijing China

Software developers and maintainers frequently conduct software refactorings to improve software quality. Identifying the conducted software refactorings may significantly facilitate the comprehension of software evolution, and thus facilitate software maintenance and evolution. Besides that, the identified refactorings are also valuable for data-driven approaches in software refactoring. To this end, researchers have proposed a few approaches to identifying software refactorings automatically. However, the performance (especially precision) of such approaches deserves substantial improvement. To this end, in this paper, we propose a novel refactoring detection approach, called REEXTRACTOR+. At the heart of REEXTRACTOR+ is a reference-based entity matching algorithm that matches coarse-grained code entities (e.g., classes and methods) between two successive versions, and a context-aware statement matching algorithm that matches statements within a pair of matched methods. We evaluated REEXTRACTOR+ on a benchmark consisting of 400 commits from 20 real-world projects. The evaluation results suggested that REEXTRACTOR+ significantly outperformed the state of the art in refactoring detection, reducing the number of false positives by 59.6% and improving recall by 19.2%. We also evaluated the performance of the proposed matching algorithms that serve as the cornerstone of refactoring detection. The evaluation results suggested that the proposed algorithms excel in matching code entities, substantially reducing the number of mistakes (false positives plus false negatives) by 67% compared to the state-of-the-art approaches. © 2025 IEEE.

关键词： Software quality

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WAS:improved white-box cryptographic algorithm over AS iteration

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Cybersecurity 2025年第1期8卷 142-152页

作者： Yatao Yang Yuying Zhai Hui Dong Yanshuo Zhang Department of Electronic and Communication Engineering Beijing Electronic Science and Technology InstituteBeijing 100070China School of Telecommunication Engineering Xidian UniversityXi’an 710071China

The attacker in white-box model has full access to software implementation of a cryptographic algorithm and full control over its execution *** order to solve the issues of high storage cost and inadequate security about most current white-box cryptographic schemes,WAS,an improved white-box cryptographic algorithm over AS iteration is *** scheme utilizes the AS iterative structure to construct a lookup table with a fvelayer ASASA structure,and the maximum distance separable matrix is used as a linear layer to achieve complete difusion in a small number of *** can be prevented from recovering the key under black-box *** length of nonlinear layer S and afne layer A in lookup table is 16 bits,which efectively avoids decomposition attack against the ASASA structure and makes the algorithm possess anti-key extraction security under the whitebox model,while WAS possesses weak white-box(32 KB,112)-space hardness to satisfy anti-code lifting *** has provable security and better storage cost than existing schemes,with the same anti-key extraction security and anti-code lifting security,only 128 KB of memory space is required in WAS,which is only 14%of SPACE-16 algorithm and 33%of Yoroi-16 algorithm.

关键词： White-box cryptography Block cipher Substitution permutation network structure Anti-key extraction Anti-code lifting

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High Spatio-Temporal Imaging Reconstruction for Hybrid Spike-RGB Cameras

IEEE Transactions on Computational Imaging

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IEEE Transactions on Computational Imaging 2025年 11卷 586-598页

作者： Xia, Lujie Xiong, Ruiqin Zhao, Jing Wang, Lizhi Zhu, Shuyuan Fan, Xiaopeng Huang, Tiejun Peking University National Engineering Research Center of Visual Technology School of Computer Science Beijing100871 China Beijing Normal University School of Artificial Intelligence Beijing100875 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Department of Computer Science and Technology Harbin150001 China Peng Cheng Laboratory Shenzhen518055 China

The acquisition of high-resolution image sequence for dynamic scenes of fast motion remains challenging due to motion blur caused by fast object movement. As a novel neuromorphic sensor, spike camera records the changing light intensity via spike stream of ultra-high temporal resolution, excelling in motion recording but limited in spatial resolution. This paper proposes a method for high spatio-temporal resolution (HSTR) imaging with a hybrid Spike-RGB camera, utilizing the information from spike stream to enhance the temporal resolution and the information from RGB images to enhance the spatial resolution of texture details. For this purpose, we present HSTR-Net, a dedicated network to process the spike and RGB data, which incorporates three key innovations: 1) A temporal control encoder enabling flexible temporal reconstruction through spike stream processing with embedded time parameters, eliminating the requirement to train multiple inference models;2) Motion-aware feature projection that aligns RGB frame details to target timestamps using spike-derived motion offsets;3) An adaptive transformer-based fusion strategy establishing cross-modal spatial correlations through mutual attention mechanisms. Extensive experiments demonstrate state-of-the-art performance on synthetic benchmark datasets with 5.23 dB PSNR and 6.94% SSIM improvement. It also shows visually impressive performance on real-world captured spike dataset. © 2015 IEEE.

关键词： High speed cameras

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