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8th International Conference on data science in Cyberspace, DSC 2023

作者： Zhang, Zhaowei Yu, Wenjing Wang, Zibin Xiang, Youlin National Key Laboratory of Science and Technology on Information System Security Beijing China School of Computer Science Fudan University Shanghai Key Laboratory of Data Science Shanghai China

ISBN: (纸本)9798350331035

Currently, protocol fuzzing techniques mainly employ two approaches: greybox fuzzing based on mutation and blackbox fuzzing based on generation. Greybox fuzzing techniques use message exchanges between the protocol server and actual clients as seeds, and generate test cases through mutation. Although this approach can provide coverage information of the SUT's code and state space through instrumentation and feedback, its drawback lies in the relatively random mutation strategy, which makes it challenging to validate the SUT's message verification process. This paper addresses this limitation by utilizing artificial intelligence techniques to extract protocol state machines. It aims to overcome the reliance on manual work in blackbox fuzzing based on generation and leverage its advantages to generate more effective fuzzing test cases. The study utilizes Prompt-Learning technology to analyze the semantic information in protocol RFC documents, obtain corresponding intermediate representations, and extract protocol state machines from these representations. Taking the BGP protocol as an experimental subject, the experimental results demonstrate a certain level of accuracy in the obtained protocol state machines and the ability to generate test cases from these state machines, thereby enhancing the automation level of protocol fuzzing. © 2023 IEEE.

关键词： fuzzing network protocols network security protocol testing state machine extraction

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Emergency Vehicle Detection Using Deep ConvNet2D and computer Vision 2

Emergency Vehicle Detection Using Deep ConvNet2D and Compute...

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2nd International Conference on Emerging Trends in Information Technology and Engineering, ic-ETITE 2024

作者： Poongavanam, Elumalaivasan Prathik, A. Munirathinam, T. Kayalvizhi, V. Rajesh, R. Mahavishnu, C. Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai Kattankulathur India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Sona College of Technology Department of Information Technology Salem India School of Computer Science and Engineering and Information Science Presidency University Bangalore India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Shreenivasa Engineering College Department of Computer Science and Engineering Dharmapuri India

ISBN: (纸本)9798350328202

A surveillance system detects emergency vehicles stuck in traffic. This system helps manage traffic because the number of vehicles on the road has been increasing daily for years, causing congestion. This project implements Deep ConvNet2D (Convolutional Network 2D) and computer Vision emergency vehicle recognition. We propose a CNN-based real-time image processing model for emergency vehicle detection. The signal control unit can be set to terminate the round robin sequence when an emergency vehicle is detected. A CNN trained on Indian ambulance images solves the problem. Tensor Flow, a Python library, was used for training. Our method detects and classifies emergency cars well. Existing systems use ANN algorithm, which is inaccurate and inefficient. The system uses Deep ConvNet2D Algorithm. The proposed real-time system is accurate. The proposed system loads and executes faster than the existing system. The system is efficient, scalable, and enhanced for complex use cases. © 2024 IEEE.

关键词： Real time systems

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Simultaneous Speech Extraction for Multiple Target Speakers Under Meeting Scenarios

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Journal of Shanghai Jiaotong University (science) 2024年 1-7页

作者： Zeng, Bang Suo, Hongbin Wan, Yulong Li, Ming School of Computer Science Wuhan University Wuhan430027 China Suzhou Municipal Key Laboratory of Multimodal Intelligent Systems Duke Kunshan University Kunshan Jiangsu215316 China Data & amp AI Engineering System OPPO Beijing100125 China

The common target speech separation directly estimates the target source, ignoring the interrelationship between different speakers at each frame. We propose a multiple-target speech separation (MTSS) model to simultaneously extract each speaker’s voice from the mixed speech rather than just optimally estimating the target source. Moreover, we propose a speaker diarization (SD) aware MTSS system (SD-MTSS). By exploiting the target speaker voice activity detection (TSVAD) and the estimated mask, our SD-MTSS model can extract the speech signal of each speaker concurrently in a conversational recording without additional enrollment audio in advance. Experimental results show that our MTSS model achieves improvements of 1.38 dB signal-to-distortion ratio (SDR), 1.34 dB scale-invariant signal-to-distortion ratio (SISDR), and 0.13 perceptual evaluation of speech quality (PESQ) over the baseline on the WSJ0-2mix-extr dataset, separately. The SD-MTSS system makes a 19.2% relative speaker dependent character error rate reduction on the Alimeeting dataset. © Shanghai Jiao Tong University 2024.

关键词： Speech analysis

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Proximal Policy Optimization-Based Intrusion Detection Model for Satellite Networks

Proximal Policy Optimization-Based Intrusion Detection Model...

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computer Engineering and Intelligent Communications (ISCEIC), International Symposium on

作者： Shuaishuai Liang Huibin Feng Jiawei Wu Canfei He College of Computer and Data Science Fuzhou University School of Computer and Big Data Minjiang University Fuzhou China School of Computer and Big Data Minjiang University Fuzhou China

ISBN: (数字)9798331518677

ISBN: (纸本)9798331518684

As satellite network communication systems become an increasingly pivotal role in modern life, The routine maintenance of satellite networks is challenging due to limited resources and their susceptibility to interference. Satellite networks are more reliant on interruption detection systems than terrestrial networks. The importance of interruption detection systems in satellite networks is becoming increasingly evident. In this paper, we propose a novel intrusion detection model based on Proximal Policy Optimization (PPO). This method interacts with the environment to learn and optimize detection strategies, allowing for dynamic adaptation to environmental changes and attack patterns. We evaluated our model on benchmark datasets and compared its performance metrics against existing models. The results demonstrate that our proposed model significantly enhances overall accuracy and precision, and outperforms baseline models in terms of training convergence speed.

关键词： Training Adaptation models Privacy Satellites Accuracy Computational modeling Intrusion detection Stability analysis Robustness Protection

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mVulPreter: A Multi-Granularity Vulnerability Detection System With Interpretations

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IEEE Transactions on Dependable and Secure Computing 2022年 1-12页

作者： Zou, Deqing Hu, Yutao Li, Wenke Wu, Yueming Zhao, Haojun Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China School of Computer Science and Engineering Nanyang Technological University Singapore 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

Due to the powerful automatic feature extraction, deep learning-based vulnerability detection methods have evolved significantly in recent years. However, almost all current work focuses on detecting vulnerabilities at a single granularity (i.e., slice-level or function-level). In practice, slice-level vulnerability detection is fine-grained but may contain incomplete vulnerability details. Function-level vulnerability detection includes full vulnerability semantics but may contain vulnerability-unrelated statements. Meanwhile, they pay more attention to predicting whether the source code is vulnerable and cannot pinpoint which statements are more likely to be vulnerable. In this paper, we design mVulPreter, a multi-granularity vulnerability detector that can provide interpretations of detection results. Specifically, we propose a novel technique to effectively blend the advantages of function-level and slice-level vulnerability detection models and output the detection results' interpretation only by the model itself. We evaluate mVulPreter on a dataset containing 5,310 vulnerable functions and 7,601 non-vulnerable functions. The experimental results indicate that mVulPreter outperforms existing state-of-the-art vulnerability detection approaches (i.e., Checkmarx, FlawFinder, RATS, TokenCNN, StatementLSTM, SySeVR, and Devign). IEEE

关键词： Semantics

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Reversible data hiding scheme using prediction neural network and adaptive modulation mapping

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Multimedia Tools and Applications 2025年第9期84卷 6665-6686页

作者： Chen, Si-Sheng Chang, Chin-Chen Horng, Ji-Hwei School of Big Data and Artificial Intelligence Fujian Polytechnic Normal University Fujian Fuzhou35030 China Department of Information Engineering and Computer Science Feng Chia University Taichung40724 Taiwan Department of Electrical Engineering National Quemoy University Kinmen89250 Taiwan

Prediction error expansion (PEE) is an attractive approach for reversible data hiding (RDH). The key issue for PEE-based RDH is to improve the prediction accuracy and design a better modulation mapping. This paper proposes a novel prediction error modulation (PEM) scheme, which comprises a prediction neural network and a modulation mapping rule generation algorithm. We use the multi-scale feature extraction (MSFE) module and the residual dense networks (RDN) to construct the prediction neural network. In the proposed RDH scheme, the cover image is divided into two parts, the reference pixel set and the cover pixel set, based on the chessboard pattern. The prediction network serves to predict the values of the cover pixel set using the reference pixel set. An optimization model is set up to adaptively determine the optimal modulation mapping for data hiding based on the specific prediction-error histogram and payload constraint. Experimental results show the superiority of the proposed prediction neural network and the optimized modulation mapping compared with related works. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Mapping

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GestureID:Gesture-Based User Authentication on Smart Devices Using Acoustic Sensing

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Sound & Vibration 2024年第1期58卷 151-169页

作者： Jizhao Liu Jiang Hui Zhaofa Wang School of Computer Science Zhongyuan University of TechnologyZhengzhou450007China Henan Development and Innovation Laboratory of Industrial Internet Security Big Data(Zhongyuan University of Technology) Zhengzhou450007China

User authentication on smart devices is crucial to protecting user privacy and device *** to the development of emerging attacks,existing physiological feature-based authentication methods,such as fingerprint,iris,and face recognition are vulnerable to forgery and *** this paper,GestureID,a system that utilizes acoustic sensing technology to distinguish hand features among users,is *** involves using a speaker to send acoustic signals and a microphone to receive the echoes affected by the reflection of the hand movements of the *** ensure system accuracy and effectively distinguish users’gestures,a second-order differential-based phase extraction method is *** method calculates the gradient of received signals to separate the effects of the user’s hand movements on the transmitted signal from the background ***,the secondorder differential phase and phase-dependent acceleration information are used as inputs to a Convolutional Neural Networks-Bidirectional Long Short-Term Memory(CNN-BiLSTM)model to model hand motion *** decrease the time it takes to collect data for new user registration,a transfer learning method is *** involves creating a user authentication model by utilizing a pre-trained gesture recognition *** a result,accurate user authentication can be achieved without requiring extensive amounts of training *** demonstrate that GestureID can achieve 97.8%gesture recognition accuracy and 96.3%user authentication accuracy.

关键词： Acoustic sensing hand gesture user authentication

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Measurement Study on Performance of Serverless Platforms

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Ruan Jian Xue Bao/Journal of Software 2025年第5期36卷 1974-2005页

作者： Wen, Jin-Feng Chen, Zhen-Peng Liu, Yi Liu, Xuan-Zhe School of Computer Science Peking University Beijing100871 China Department of Computer Science University College London LondonWC1E 6BT United Kingdom Advanced Institute of Big Data Beijing100091 China

Serverless computing is an emerging paradigm of cloud computing, allowing developers to focus only on application logic development without the need to manage complex underlying tasks. This paradigm allows developers to quickly build smaller-granularity applications, the one at the function level. With the increasing popularity of serverless computing, major cloud computing vendors have introduced their commercial serverless platforms one after another. However, the characteristics of these platforms have yet to be systematically studied and reliably compared. A comprehensive analysis of these characteristics can help developers choose an appropriate serverless platform while developing and executing serverless applications in the right way. To this end, an empirical study is conducted on the characteristics of mainstream commercial serverless platforms. This study involves such mainstream serverless platforms as AWS Lambda, Google Cloud Functions, Microsoft Azure Functions, and Alibaba Function Compute. This study is divided into two major parts: feature summarization and runtime performance analysis. In the feature summarization, the official documents of these serverless platforms are discussed and their key features are summarized and compared in terms of development, deployment, and runtime. In the runtime performance analysis, representative benchmarks are applied to analyze the runtime performance offered by these serverless platforms on a multidimensional basis. Specifically, key factors for the cold-start performance of the applications are first analyzed, such as programming languages and memory sizes. Furthermore, the tasks-executing performance of serverless platforms is discussed. Based on the results of feature summarization and runtime performance analysis, this study sums up a series of findings and provides practical insights and potential research opportunities for developers, cloud computing vendors, and researchers. © 2025 Chinese Academy of Scie

关键词： Application programs

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Emotional Reactivity Classification Using Artificial Intelligence Based on Twitter in Bahasa Indonesia 3

Emotional Reactivity Classification Using Artificial Intelli...

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3rd IEEE International Conference on Social sciences and Intelligence Management, SSIM 2023

作者： Paramita, Greta V. Andangsari, Esther W. Kemala, Ade P. Ali, Moondore M. Putri, Trisa M. Puspita, Katarina I. Bina Nusantara University Faculty of Humanities Psychology Department Jakarta Indonesia Data Science Program School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798350327786

Twitter is one of Indonesia's most widely used social media to express positive and negative emotions. Negative emotions are referred to as emotional reactivity, an example of which is depression. To detect emotional reactivity, Artificial Intelligence (AI) was used. With deep learning pretrained model methods (IndoBERT and IndoBERTweet), AI successfully classified emotional reactivity. AI satisfactorily detected emotional reactivity on Twitter. © 2023 IEEE.

关键词： Deep learning

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Improving Alzheimer's Disease Diagnosis on Brain MRI Scans with an Ensemble of Deep Learning Models 1

Improving Alzheimer's Disease Diagnosis on Brain MRI Scans w...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Reza, Md. Sazid Jahangir Kabir, Mir Md. Rakib Mollah, Md. Abdur Rajshahi University of Engineering and Technology Department of Computer Science and Engineering Rajshahi Bangladesh Td School University of Technology Sydney Data Science and Innovation Sydney Australia

ISBN: (纸本)9798350330915

Alzheimer's disease (AD) is a widespread neurolog-ical condition affecting millions globally. It gradually advances, leading to memory loss, cognitive deterioration, and a substantial decline in overall quality of life for those affected. AD patients experience memory decline, eroding cherished memories and straining relationships, while daily tasks become challenging. Numerous investigations have been conducted in this field, as the timely identification of Alzheimer's disease at its initial stage is of the utmost importance. A major limitation in this field is the predominant emphasis on using single fine-tuned CNN architecture or comparing pre-trained and custom CNN models for Alzheimer's detection, often on small datasets, which neglects a more comprehensive approach. Using smaller datasets can negatively impact deep learning modeling accuracy due to overfitting, limited representation, and poor generalization. This study addresses the current research problems and proposes an ensemble approach that combines predictions from various pre-trained models, including DenseNet-121, EfficientNet B7, ResNet-50, VGG-19, and also from a Custom CNN. The model averaging ensemble method was applied, a subset of the Stacking Ensemble, to two ADNI datasets, with dataset-I being the larger. The goal was to assess the efficacy of this ensemble approach for accurate multiclass classification on ADNI datasets, where it successfully identified all classes despite differing sample volumes. A vast experiment was conducted on two distinct and widely recognized real-world datasets, resulting in accuracies of 99.96% and 98.90% respectively. Finally, the outcome of the research compared with recent research findings demonstrates the potential of our approach in advancing Alzheimer's disease detection by outperforming other benchmark approaches by a significant margin. © 2023 IEEE.

关键词： ADNI DenseNet-121 EfficientNet B7 Ensemble Model Averaging Predictions ResNet-50 VGG-19

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