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International Joint Conference on Neural Networks (IJCNN)

作者： Yuecheng Wen Xiaohui Han Wenbo Zuo Weihua Liu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Quancheng Provincial Laboratory Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

In the realm of cybersecurity, efficiently and precisely identifying and mitigating potential threats from malicious encrypted traffic is crucial. As deep learning evolves, methods relying on Convolutional Neural Networks (CNNs) to convert traffic payloads into visual representations for analysis have become popular. Although this technique effectively discerns malicious encrypted traffic, it faces inherent limitations, such as difficulties in deployment due to extensive network scales and high computational demands, especially in edge computing environments. To address these challenges, we propose a model for maliciously encrypted traffic detection, named LightMETD. The model consists of two stages. In the first stage, we utilize Random Forest (RF) with a feature selection algorithm to quickly identify easily distinguishable traffic patterns. In the second stage, the filtered traffic data is converted into grayscale images and classified using an innovative lightweight MobileNetV3-S architecture. Our LightMETD model addresses the problem of large volume and high computational requirements of CNN models. To enhance the effectiveness of the LightMETD, we propose an innovative knowledge distillation method to train LightMETD. This method significantly improves the accuracy of malicious encrypted traffic detection by designing a distillation loss function that enables the model to better capture the intricate relationships between samples. We use a USTC-TFC2016 public traffic dataset and a locally collected dataset to demonstrate the effectiveness of LightMETD. LightMETD achieves an impressive 97% average classification accuracy. LightMETD significantly outperforms other baselines in terms of volume and detection speed, underscoring its viability and superiority in addressing real-world challenges posed by malicious encrypted traffic.

关键词： Training Solid modeling Visualization Accuracy Computational modeling Telecommunication traffic Traffic control

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SFMNet:focusing on feature attention for image classification 9

SFMNet:focusing on feature attention for image classificatio...

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9th International Conference on Digital Home, ICDH 2022

作者： Wu, Wenwei Luo, Xiaonan Xu, Songhua Chen, Ruiai School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guilin Huigu Institute of Artificial Intelligence Industrial Technology Guilin China Zhaoqing Br. Center of Guangdong Laboratory for Lingnan Modern Agricultural Science and Technology China Guilin University of Electronic Technology National Local Joint Engineering Research Center of Satellite Navigation and Location Service Guilin541004 China

ISBN: (数字)9781665454780

ISBN: (纸本)9781665454780

In the process of convolution network processing, a convolution of each layer can be regarded as a mapping operation of image features. As we all know, when the human eye maps the prescene capture to the brain neurons, the perception of different parts of a scene is different, especially when the perspective focuses on a part or the characteristics of an object, the visual cortex pays more attention to this part. In the construction of CNN, there is consideration of image perception field, but little consideration of image feature area. Therefore, this paper proposes a convolution module called "select future map"(SFM). The model uses the residual idea of RESNET network and the use of channel attention in SENet network, and develops the attention to its own characteristic attention. The structure of convolution module is divided into three parts. The first part is multi module feature convolution, which is used to extract the features of each module in the image. The second part is feature processing, which is used to pay attention to the proportion of different module features. The third part is feature aggregation, which multiplies the modular convolution and the corresponding specific gravity to obtain the next feature image. Taking this as the basic unit, add modules to form a network, and the final experimental results get an accuracy of 84.12%. © 2022 IEEE.

关键词： Convolution

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A residual attention-based privacy-preserving biometrics model of transcriptome prediction from genome

A residual attention-based privacy-preserving biometrics mod...

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

作者： Cheng Tian Song Liu Jinbao Li Guangchen Wang Luyue Kong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Department of Medicine Shandong Medical College Jinan China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Transcriptome prediction from genetic variation data is an important task in the privacy-preserving and biometrics field, which can better protect genomic data and achieve biometric recognition through transcriptome. Many transcriptome prediction methods have achieved good accuracy from genetic variation data. However, these traditional transcriptome prediction methods have the problems of linear assumption, overfitting, expose personal privacy, and extensive manual optimization. To solve these shortcomings, we propose an attention-based transcriptome prediction model from genetic variation named RATPM that improves the accuracy of transcriptome prediction and protects participant genomic data. In RATPM, we introduce and improve the deep learning model with multi-head self-attention into the transcriptome prediction stage of Predixcan, which uncovers the non-linear relationship between genetic variation and transcriptome. Moreover, we introduce a residual attention module to generate attention-aware features and extract more accurate features at different levels from genetic variation. Furthermore, we introduce the BERT pre-training module to encode genetic variation fully utilizing their contextual information. Our research enables scientific institutions to publish only predicted transcriptomic data for biometric purposes, thus protecting the genomic information of the subjects. Finally, we evaluated our model on the 1000 Genomes and Geuvadis projects datasets to compare with other baseline models.

关键词： Privacy Biometrics (access control) Biological system modeling Genomics Predictive models Feature extraction Gene expression

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PTGFI: A Prompt-Based Two-Stage Generative Framework for Function Name Inference

PTGFI: A Prompt-Based Two-Stage Generative Framework for Fun...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Menglu Wang Xiaohui Han Peipei Wang Wenbo Zuo Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Quan Cheng Laboratory Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In the field of cybersecurity, analyzing malicious software or programs is crucial for preventing network attacks. Malicious code often exists in a stripped binary form to thwart analysis, presenting challenges for analysts. This study investigates inferring function names from stripped binary to aid security researchers in analyzing malicious code. We propose PTGFI, a Prompt-based Two-stage Generative framework for Function name Inference. The PTGFI framework transforms the task of inferring function names into a two-stage semantic generation problem. By capturing function descriptions of assembly functions and introducing prompt learning, effective inference of function names is achieved. In experiments, PTGFI outperforms the state-of-the-art model by 2.96 % in precision. Moreover, ablation studies demonstrate the effectiveness of advanced components within the PTGFI framework. We further validate the utility and reliability of function names generated by the PTGFI framework through case studies.

关键词： Analytical models Codes Accuracy Semantics Transforms Malware Reliability computer crime Assembly Cybernetics

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SalientPoint: A Self-Supervised Keypoint Detection Based on Saliency of Descriptor

SalientPoint: A Self-Supervised Keypoint Detection Based on ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Keyu Zhou Wangshu Yao Siyu Yang Siyuan Peng School of Computer Science & Technology Soochow University Suzhou China School of Software Soochow University Suzhou China Collaborative Innovation Center of Novel Software Technology and Industrialization China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

This paper introduces a novel self-supervised frame-work designed to train keypoint detectors model across various descriptor models, named SalientPoint. The core of our method includes three modules: Homographic Augmentation, Absolute Saliency Calculation and Relative Saliency Calculation. The purpose of Homographic Augmentation is mainly to providing a large number of learnable candidate keypoint labels, ensuring smooth convergence of the model. The purpose of Absolute/Relative Saliency Calculation is to select a sufficient number of reliable labels from candidate keypoint labels for different descriptors. SalienPoint provides salient keypoints which are indirectly defined by the descriptors and avoids huge computation associated with dense descriptors. Experimental results demonstrate that SalientPoint can effectively improve image matching performance of various descriptors on two benchmarks. An ablation experiment validate that each component under this framework is indispensable.

关键词： Training Computational modeling Image matching Neural networks Detectors Benchmark testing Computational efficiency

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Optimal Fusion-Based Handcrafted with Deep Features for Brain Cancer Classification

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computers, Materials & Continua 2022年第10期73卷 801-815页

作者： Mahmoud Ragab Sultanah M.Alshammari Amer H.Asseri Waleed K.Almutiry Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Center for Artificial Intelligence in Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Biochemistry Department Faculty of ScienceKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics College of Science and Arts in Ar RassQassim UniversityBuryadah52571Saudi Arabia

Brain cancer detection and classification is done utilizing distinct medical imaging modalities like computed tomography(CT),or magnetic resonance imaging(MRI).An automated brain cancer classification using computer aided diagnosis(CAD)models can be designed to assist *** the recent advancement in computer vision(CV)and deep learning(DL)models,it is possible to automatically detect the tumor from images using a computer-aided *** study focuses on the design of automated Henry Gas Solubility Optimization with Fusion of Handcrafted and Deep Features(HGSO-FHDF)technique for brain cancer *** proposed HGSO-FHDF technique aims for detecting and classifying different stages of brain *** proposed HGSO-FHDF technique involves Gabor filtering(GF)technique for removing the noise and enhancing the quality of MRI *** addition,Tsallis entropy based image segmentation approach is applied to determine injured brain regions in the MRI ***,a fusion of handcrafted with deep features using Residual Network(ResNet)is utilized as feature ***,HGSO algorithm with kernel extreme learning machine(KELM)model was utilized for identifying the presence of brain *** examining the enhanced brain tumor classification performance,a comprehensive set of simulations take place on the BRATS 2015 dataset.

关键词： Brain cancer medical imaging deep learning fusion model metaheuristics feature extraction handcrafted features

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International Conference on computer Supported Cooperative Work in Design

作者： Wei Ding Luyao Wang Wei Zhang Myung Jin Lee Ling Wang Kwang Woo Nam Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Industry-University Cooperstion Foundation Chungbuk National University Cheongju Korea School of Computer Science Northeast Electric Power University Jilin China School of Computer Science and Engineering Kunsan National University Gunsan Korea

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

In this paper, we propose a new UAV video similarity measurement algorithm to cluster and analyze UAV videos by measuring the similarity between UAV videos and train them in sub-groups, which in turn improves the efficiency of UAV video data mining. Our preliminary work introduces the maximum common view subsequence (LCVS) to compute the similarity between UAV videos, but LCVS needs to frequently compute the common visible region of video frames generating extremely high time cost. In order to solve the problem of high computational cost of LCVS, we propose a similarity algorithm based on minimum tilted outer rectangle clustering (MTORC) for UAV videos. The MTORC algorithm adopts minimum tilted outer rectangle to cluster the field of view of the marked points in each frame, which reduces the time complexity of calculating the common visible region. By comparing experiments with LCSS and LCVS, the experimental results prove that MTORC is better than the present previous research in terms of algorithm accuracy and time cost.

关键词： Training Visualization Costs Accuracy Clustering algorithms Autonomous aerial vehicles Time measurement

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Page Layout Analysis of Complex Document Images 15

Page Layout Analysis of Complex Document Images

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Swarup, Lakshya Shalini, M. Chaudhary, Anisha Adarsha, H. Wanjale, Kirti H. Yuvaraj, K. Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Quantum University Research Center Quantum University India School of Engineering and Technology Mechaical Engineering Karnataka Bangalore India Vishwakarma Institute of Technology Department of Computer Engineering Pune India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India

ISBN: (纸本)9798350370249

Page format evaluation of complex document pictures is the process of information on the shape of report pictures and extracting the visible content material inside them. General page format, text partitioning, and vicinity labeling, font identification. As an award, it may be used to enhance the functionality of record processing obligations, as well asal and optical character popularity. It provides automatic methods for parsing the layout of core components, such as text blocks, picspilt into files and tables, and mathematical formulas. The process of page layout evaluation retrieves actual relationships among visible items to understand the semantic content material of a document. A typical page layout analysis pipeline comprises low-level tasks, including image pre-processing and noise reduction, by mid-degree obligations such as textual content segmentation, place labeling, and taking location followed by the aid of font identity. Finally, the output we require is only created using higher-level duties such as table extraction and text monitoring. The synthesis of the two responsibilities allows for a greater understanding of page shape, enabling stepped-forward record retrieval and optical character recognition. Additionally, this can make documentation available in other zones, including summary records and page categorization. © 2024 IEEE.

关键词： Combination Evaluation Material Mathematical Responsibilities Segmentation

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Owl: Differential-Based Side-Channel Leakage Detection for CUDA Applications

Owl: Differential-Based Side-Channel Leakage Detection for C...

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International Conference on Dependable Systems and Networks (DSN)

作者： Yu Zhao Wenjie Xue Weijie Chen Weizhong Qiang Deqing Zou Hai Jin National Engineering Research Center for Big Data Technology and System Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Jinyinhu Laboratory Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350341058

ISBN: (纸本)9798350341065

Over the past decade, various methods for detecting side-channel leakage have been proposed and proven to be effective against CPU side-channel attacks. These methods are valuable in assisting developers to identify and patch side-channel vulnerabilities. Nevertheless, recent research has revealed the feasibility of exploiting side-channel vulnerabilities to steal sensitive information from GPU applications, which are beyond the reach of previous side-channel detection methods. Therefore, in this paper, we conduct an in-depth examination of various GPU features and present Owl, a novel side-channel detection tool targeting CUDA applications on NVIDIA GPUs. Owl is designed to detect and locate side-channel leakage in various types of CUDA applications. When tracking the execution of CUDA applications, we design a hierarchical tracing scheme and extend the A-DCFG (Attributed Dynamic Control Flow Graph) to address the massively parallel execution in CUDA, ensuring Owl's detection scalability. After completing the initial assessment and filtering, we conduct statistical tests on the differences in program traces to determine whether they are indeed caused by input variations, subsequently facilitating the positioning of side-channel leaks. We evaluate Owl's capability to detect side-channel leaks by testing it on Libgpucrypto, PyTorch, and nvJPEG. Meanwhile, we verify that our solution effectively handles a large number of threads. Owl has successfully identified hundreds of leaks within these applications. To the best of our knowledge, we are the first to implement side-channel leakage detection for general CUDA applications.

关键词： Target tracking Statistical analysis Filtering Scalability Graphics processing units Side-channel attacks Feature extraction

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CaptchaGG: A linear graphical CAPTCHA recognition model based on CNN and RNN 9

CaptchaGG: A linear graphical CAPTCHA recognition model base...

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9th International Conference on Digital Home, ICDH 2022

作者： Chen, Yang Luo, Xiaonan Xu, Songhua Chen, Ruiai Guilin University of Electronic Technology School of Computer Science and Information Security China Zhaoqing Br. Center of Guangdong Laboratory for Lingnan Modern Agricultural Science and Technology China Guilin Huigu Institute of Artificial Intelligence Industrial Technology Guilin China Guilin University of Electronic Technology National Local Joint Engineering Research Center of Satellite Navigation and Location Service Guilin541004 China

ISBN: (数字)9781665454780

ISBN: (纸本)9781665454780

This paper presents CaptchaGG, a model for recognizing linear graphical CAPTCHAs. As in the previous society, CAPTCHA is becoming more and more complex, but in some scenarios, complex CAPTCHA is not needed, and usually, linear graphical CAPTCHA can meet the corresponding functional scenarios, such as message boards of websites and registration of accounts with low security. The scheme is based on convolutional neural networks for feature extraction of CAPTCHAs, recurrent neural forests A neural network that is too complex will lead to problems such as difficulty in training and gradient disappearance, and too simple will lead to underfitting of the model. For the single problem of linear graphical CAPTCHA recognition, the model which has a simple architecture, extracting features by convolutional neural network, sequence modeling by recurrent neural network, and finally classification and recognition, can achieve an accuracy of 96% or more recognition at a lower complexity. © 2022 IEEE.

关键词： Feature extraction

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