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Combo Packet:An Encryption Traffic Classification Method Based on Contextual Information

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Computers, Materials & Continua 2024年第4期79卷 1223-1243页

作者： Yuancong Chai Yuefei Zhu Wei Lin Ding Li State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering UniversityZhengzhou450001China

With the increasing proportion of encrypted traffic in cyberspace, the classification of encrypted traffic has becomea core key technology in network supervision. In recent years, many different solutions have emerged in this *** methods identify and classify traffic by extracting spatiotemporal characteristics of data flows or byte-levelfeatures of packets. However, due to changes in data transmission mediums, such as fiber optics and satellites,temporal features can exhibit significant variations due to changes in communication links and transmissionquality. Additionally, partial spatial features can change due to reasons like data reordering and *** with these challenges, identifying encrypted traffic solely based on packet byte-level features is significantlydifficult. To address this, we propose a universal packet-level encrypted traffic identification method, ComboPacket. This method utilizes convolutional neural networks to extract deep features of the current packet andits contextual information and employs spatial and channel attention mechanisms to select and locate effectivefeatures. Experimental data shows that Combo Packet can effectively distinguish between encrypted traffic servicecategories (e.g., File Transfer Protocol, FTP, and Peer-to-Peer, P2P) and encrypted traffic application categories (e.g.,BitTorrent and Skype). Validated on the ISCX VPN-non VPN dataset, it achieves classification accuracies of 97.0%and 97.1% for service and application categories, respectively. It also provides shorter training times and higherrecognition speeds. The performance and recognition capabilities of Combo Packet are significantly superior tothe existing classification methods mentioned.

关键词： Encrypted traffic classification packet-level convolutional neural network attention mechanisms

A Study on Enhancing Chip Detection Efficiency Using the Lightweight Van-YOLOv8 Network

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Computers, Materials & Continua 2024年第4期79卷 531-547页

作者： Meng Huang Honglei Wei Xianyi Zhai School of Mechanical Engineering and Automation Dalian Polytechnic UniversityDalian116034China

In pursuit of cost-effective manufacturing,enterprises are increasingly adopting the practice of utilizing recycled semiconductor *** ensure consistent chip orientation during packaging,a circular marker on the front side is employed for pin alignment following successful functional ***,recycled chips often exhibit substantial surface wear,and the identification of the relatively small marker proves ***,the complexity of generic target detection algorithms hampers seamless *** these issues,this paper introduces a lightweight YOLOv8s-based network tailored for detecting markings on recycled chips,termed ***,to alleviate the influence of diminutive,low-resolution markings on the precision of deep learning models,we utilize an upscaling approach for enhanced *** technique relies on the Super-Resolution Generative Adversarial Network with Extended Training(SRGANext)network,facilitating the reconstruction of high-fidelity images that align with input ***,we replace the original YOLOv8smodel’s backbone feature extraction network with the lightweight VanillaNetwork(VanillaNet),simplifying the branch structure to reduce network ***,a Hybrid attention Mechanism(HAM)is implemented to capture essential details from input images,improving feature representation while concurrently expediting model inference *** results demonstrate that the Van-YOLOv8 network outperforms the original YOLOv8s on a recycled chip dataset in various ***,it demonstrates superiority in parameter count,computational intricacy,precision in identifying targets,and speed when compared to certain prevalent algorithms in the current *** proposed approach proves promising for real-time detection of recycled chips in practical factory settings.

关键词： Lightweight neural networks attention mechanisms image super-resolution enhancement feature extraction small object detection

Mobile Crowdsourcing Task Allocation Based on Dynamic Self-attention GANs

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Computers, Materials & Continua 2024年第4期79卷 607-622页

作者： Kai Wei Song Yu Qingxian Pan School of Computer and Control Engineering Yantai UniversityYantai264005China School of Computer and Information Science Southwest UniversityChongqing400715China

Crowdsourcing technology is widely recognized for its effectiveness in task scheduling and resource *** traditional methods for task allocation can help reduce costs and improve efficiency,they may encounter challenges when dealing with abnormal data flow nodes,leading to decreased allocation accuracy and *** address these issues,this study proposes a novel two-part invalid detection task allocation *** the first step,an anomaly detection model is developed using a dynamic self-attentive GAN to identify anomalous *** to the baseline method,the model achieves an approximately 4%increase in the F1 value on the public *** the second step of the framework,task allocation modeling is performed using a twopart graph matching *** phase introduces a P-queue KM algorithm that implements a more efficient optimization *** allocation efficiency is improved by approximately 23.83%compared to the baseline *** results confirm the effectiveness of the proposed framework in detecting abnormal data nodes,enhancing allocation precision,and achieving efficient allocation.

关键词： Mobile crowdsourcing task allocation anomaly detection GAN attention mechanisms

Enhancing Deep Learning Semantics:The Diffusion Sampling and Label-Driven Co-attention Approach

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Computers, Materials & Continua 2024年第5期79卷 1939-1956页

作者： ChunhuaWang Wenqian Shang Tong Yi Haibin Zhu State Key Laboratory of Media Convergence and Communication Communication University of ChinaBeijing100024China School of Computer and Cyber Sciences Communication University of ChinaBeijing100024China School of Computer Science and Engineering Guangxi Normal UniversityGuilin541004China Department of Computer Science Nipissing UniversityNorth BayON P1B 8L7Canada

The advent of self-attention mechanisms within Transformer models has significantly propelled the advancement of deep learning algorithms,yielding outstanding achievements across diverse ***,self-attention mechanisms falter when applied to datasets with intricate semantic content and extensive dependency *** response,this paper introduces a Diffusion Sampling and Label-Driven Co-attention Neural Network(DSLD),which adopts a diffusion sampling method to capture more comprehensive semantic information of the ***,themodel leverages the joint correlation information of labels and data to introduce the computation of text representation,correcting semantic representationbiases in thedata,andincreasing the accuracyof semantic ***,the model computes the corresponding classification results by synthesizing these rich data semantic *** on seven benchmark datasets show that our proposed model achieves competitive results compared to state-of-the-art methods.

关键词： Semantic representation sampling attention label-driven co-attention attention mechanisms

Multi-Scale Binocular Stereo Matching Based on Semantic Association

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Chinese Journal of Electronics 2024年第04期33卷 1010-1022页

作者： Jin ZHENG Botao JIANG Wei PENG Qiaohui ZHANG State Key Laboratory of Virtual Reality Techonology and Systems Beihang University School of Computer Science and Engineering Beihang University

Aiming at the low accuracy of existing binocular stereo matching and depth estimation methods, this paper proposes a multi-scale binocular stereo matching network based on semantic association. A semantic association module is designed to construct the contextual semantic association relationship among the pixels through semantic category and attention mechanism. The disparity of those regions where the disparity is easily estimated can be used to assist the disparity estimation of relatively difficult regions, so as to improve the accuracy of disparity estimation of the whole image. Simultaneously, a multi-scale cost volume computation module is proposed. Unlike the existing methods, which use a single cost volume, the proposed multi-scale cost volume computation module designs multiple cost volumes for features of different scales. The semantic association feature and multi-scale cost volume are aggregated, which fuses the high-level semantic information and the low-level local detailed information to enhance the feature representation for accurate stereo matching. We demonstrate the effectiveness of the proposed solutions on the KITTI2015 binocular stereo matching dataset, and our model achieves comparable or higher matching performance, compared to other seven classic binocular stereo matching algorithms.

关键词： Costs Accuracy attention mechanisms Fuses Computational modeling Semantics Estimation

Improving VQA via Dual-Level Feature Embedding Network

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Intelligent Automation & Soft Computing 2024年第3期39卷 397-416页

作者： Yaru Song Huahu Xu Dikai Fang School of Computer Engineering and Science Shanghai UniversityShanghai200444China

Visual Question Answering(VQA)has sparked widespread interest as a crucial task in integrating vision and *** primarily uses attention mechanisms to effectively answer questions to associate relevant visual regions with input *** detection-based features extracted by the object detection network aim to acquire the visual attention distribution on a predetermined detection frame and provide object-level insights to answer questions about foreground objects more ***,it cannot answer the question about the background forms without detection boxes due to the lack of fine-grained details,which is the advantage of grid-based *** this paper,we propose a Dual-Level Feature Embedding(DLFE)network,which effectively integrates grid-based and detection-based image features in a unified architecture to realize the complementary advantages of both ***,in DLFE,In DLFE,firstly,a novel Dual-Level Self-attention(DLSA)modular is proposed to mine the intrinsic properties of the two features,where Positional Relation attention(PRA)is designed to model the position ***,we propose a Feature Fusion attention(FFA)to address the semantic noise caused by the fusion of two features and construct an alignment graph to enhance and align the grid and detection ***,we use co-attention to learn the interactive features of the image and question and answer questions more *** method has significantly improved compared to the baseline,increasing accuracy from 66.01%to 70.63%on the test-std dataset of VQA 1.0 and from 66.24%to 70.91%for the test-std dataset of VQA 2.0.

关键词： Visual question answering multi-modal feature processing attention mechanisms cross-model fusion

Irregular scene text detection via attention guided border labeling

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Science China(Information Sciences) 2019年第12期62卷 33-43页

作者： Jie CHEN Zhouhui LIAN Yizhi WANG Yingmin TANG Jianguo XIAO Wangxuan Institute of Computer Technology Peking University Center For Chinese Font Design and Research Peking University

Scene text detection plays an important role in many computer vision applications. With the help of recent deep learning techniques, multi-oriented text detection that was considered to be quite challenging has been solved to some extent. However, most existing methods still perform poorly for curved text detection, mainly due to the limitation of their text representations(e.g., horizontal boxes, rotated rectangles or quadrangles). To solve this problem, we propose a novel method to detect irregular scene texts based on instance-aware segmentation. The key idea is to design an attention guided semantic segmentation model to precisely label the weighted borders of text regions. Experiments conducted on several widely-used benchmarks demonstrate that our method achieves superior results on curved text datasets(i.e., with F-score80.1% and 78.8% for the CTW1500 and Total-Text, respectively) and obtains comparable performance on multi-oriented text datasets compared to the state-of-the-art approaches.

关键词： scene text detection weighted border attention mechanisms curved text semantic segmentation

OGSRN:Optical-guided super-resolution network for SAR image

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Chinese Journal of Aeronautics 2022年第5期35卷 204-219页

作者： Yanshan LI Li ZHOU Fan XU Shifu CHEN ATR National Key Laboratory of Defense Technology Shenzhen UniversityShenzhen 518060China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen UniversityShenzhen 518060China

Although Convolutional Neural Networks(CNNs)have significantly improved the development of image Super-Resolution(SR)technology in recent years,the existing SR methods for SAR image with large scale factors have rarely been studied due to technical difficulty.A more efficient method is to obtain comprehensive information to guide the SAR image ***,the co-registered High-Resolution(HR)optical image has been successfully applied to enhance the quality of SAR image due to its discriminative *** by this,we propose a novel Optical-Guided Super-Resolution Network(OGSRN)for SAR image with large scale ***,our proposed OGSRN consists of two sub-nets:a SAR image SuperResolution U-Net(SRUN)and a SAR-to-Optical Residual Translation Network(SORTN).The whole process during training includes two *** stage-1,the SR SAR images are reconstructed by the *** an Enhanced Residual attention Module(ERAM),which is comprised of the Channel attention(CA)and Spatial attention(SA)mechanisms,is constructed to boost the representation ability of the *** stage-2,the output of the stage-1 and its corresponding HR SAR images are translated to optical images by the SORTN,*** then the differences between SR images and HR images are computed in the optical space to obtain feedback information that can reduce the space of possible SR *** that,we can use the optimized SRUN to directly produce HR SAR image from Low-Resolution(LR)SAR image in the testing *** experimental results show that under the guidance of optical image,our OGSRN can achieve excellent performance in both quantitative assessment metrics and visual quality.

关键词： SAR image Super-resolution Optical image attention mechanisms Convolutional Nerual Networks(CNNs)

Joint Event Extraction Based on Global Event-Type Guidance and attention Enhancement

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Computers, Materials & Continua 2021年第9期68卷 4161-4173页

作者： Daojian Zeng Jian Tian Ruoyao Peng Jianhua Dai Hui Gao Peng Peng Hunan Normal University Changsha410081China Changsha University of Science&Technology Changsha410114China National University of Defense Technology Changsha410073China University of Waterloo WaterlooN2L3GICanada

Event extraction is one of the most challenging tasks in information *** is a common phenomenon where multiple events exist in the same ***,extracting multiple events is more difficult than extracting a single *** event extraction methods based on sequence models ignore the interrelated information between events because the sequence is too *** addition,the current argument extraction relies on the results of syntactic dependency analysis,which is complicated and prone to error *** order to solve the above problems,a joint event extraction method based on global event-type guidance and attention enhancement was proposed in this ***,for multiple event detection,we propose a global-type guidance method that can detect event types in the candidate sequence in advance to enhance the correlation information between *** argument extraction,we converted it into a table-flling problem,and proposed a table-flling method of the attention mechanism,that is simple and can enhance the correlation between trigger words and *** experimental results based on the ACE 2005 dataset showed that the proposed method achieved 1.6%improvement in the task of event detection,and obtained state-of-the-art results in the argument extraction task,which proved the effectiveness of the method.

关键词： Event extraction event-type guidance table flling attention mechanisms