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IEEE Transactions on Artificial Intelligence 2023年第1期4卷 148-160页

作者： Pan, Zhaoqing Yuan, Feng Wang, Xu Xu, Long Shao, Xiao Kwong, Sam the School of Electrical and Information Engineering Tianjin University Tianjin300072 China the College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China the Key Laboratory of Solar Activity National Astronomical Observatories Chinese Academy of Sciences Beijing100012 China the School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China the Department of Computer Science City University of Hong Kong Hong Kong

No-reference image quality assessment (NR-IQA) aims to evaluate image quality without using the original reference images. Since the early NR-IQA methods based on distortion types were only applicable to specific distortion scenarios, and lack of practicality, it is challenging to designing a universal NR-IQA method. In this article, a multibranch convolutional neural network (MB-CNN) based NR-IQA method is proposed, which includes a spatial-domain feature extractor, a gradient-domain feature extractor, and a weight mechanism. The spatial-domain feature extractor aims to extract the distortion features from the spatial domain. The gradient-domain feature extractor is used to guide the spatial-domain feature extractor to pay more attention to the distortions of the structure information. Particularly, the spatial-domain feature extractor uses the hierarchical feature merge module to realize multiscale feature representation, and the gradient-domain feature extractor uses pyramidal convolution to extract the multiscale structure information of the distorted image. In addition, a position vector is proposed to build the weight mechanism by considering the position relationships between patches and its entire image for improving the final prediction performance. We conduct the experiments on five representative databases: LIVE, TID2013, CSIQ, LIVE MD and Waterloo Exploration Database, and the experimental results show that the proposed NR-IQA method achieves the state-of-the-art performance, which demonstrate the effectiveness of our proposed NR-IQA method. The code ofthe proposed MB-CNN will be released at https://***/NUIST-Videocoding/MB-CNN. © 2020 IEEE.

关键词： Neural networks

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DeepOCL:A deep neural network for Object Constraint Language generation from unrestricted nature language

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CAAI Transactions on Intelligence Technology 2024年第1期9卷 250-263页

作者： Yilong Yang Yibo Liu Tianshu Bao Weiru Wang Nan Niu Yongfeng Yin School of Software Beihang UniversityBeijingChina College of Computer Science and Technology Guizhou UniversityGuiyangGuizhouChina Faculty of Information Technology Beijing University of TechnologyBeijingChina Department of Electrical Engineering and Computer Sciences University of CincinnatiCincinnatiOhioUSA

Object Constraint Language(OCL)is one kind of lightweight formal specification,which is widely used for software verification and validation in NASA and Object Management Group *** OCL provides a simple expressive syntax,it is hard for the developers to write correctly due to lacking knowledge of the mathematical foundations of the first-order logic,which is approximately half accurate at the first stage of devel-opment.A deep neural network named DeepOCL is proposed,which takes the unre-stricted natural language as inputs and automatically outputs the best-scored OCL candidates without requiring a domain conceptual model that is compulsively required in existing rule-based generation *** demonstrate the validity of our proposed approach,ablation experiments were conducted on a new sentence-aligned dataset named *** experiments show that the proposed DeepOCL can achieve state of the art for OCL statement generation,scored 74.30 on BLEU,and greatly outperformed experienced developers by 35.19%.The proposed approach is the first deep learning approach to generate the OCL expression from the natural *** can be further developed as a CASE tool for the software industry.

关键词： deep learning OCL software engineering

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Deep-USIpred: identifying substrates of ubiquitin protein ligases E3 and deubiquitinases with pretrained protein embedding and bayesian neural network

Deep-USIpred: identifying substrates of ubiquitin protein li...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Wang, Jia Pan, Gui-Qing Li, Jian-Qiang Shang, Xue-Qun You, Zhu-Hong Huang, Yu-An Shenzhen University College of Computer Science and Software Engineering Shenzhen China Northwesterm Polytechnical University School of Computer Science Shaanxi China

ISBN: (纸本)9798350337488

Identifying the substrates of ubiquitin protein ligase (E3) and deubiquitinases (DUB) contributes to the discovery of potential therapeutic targets for diseases. However, experimental identification of E3/DUB-substrate interactions is costly and time-consuming. Current computational methods for predicting E3/DUB-substrate interactions rely heavily on specific domain knowledge and involve complex and diverse biological data processing. To address this challenge, we proposed a deep learning prediction model, named Deep-USIpred, which predicts E3/DUB-substrate interactions using protein sequences. The proposed Deep-USIpred model encodes protein sequences with a pretrained model and utilizes 1DCNN-BNN deep learning algorithm to make a robust prediction model. We evaluated the performance of the proposed model on real datasets, and our experimental results show that it can achieve excellent prediction performance on the tasks of ESI and DSI. Our proposed method provides a promising alternative for the prediction of E3/DUB-substrate interactions, which has the potential to accelerate drug discovery for various diseases. The source code and dataset are available at https://***/PGTSING/Deep-USIpred. © 2023 IEEE.

关键词： bayesian neural network deep learning deubiquitinases sequence-based prediction ubiquitin protein ligases E3

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A Low-complexity DOA Estimation Method for Nested Array via Root-MUSIC 24

A Low-complexity DOA Estimation Method for Nested Array via ...

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8th International Conference on Electronic Information Technology and computer engineering, EITCE 2024

作者： Huang, Juntan Xu, Shufang College of Electrical and Power Engineering Hohai University Jiangsu Nanjing China College of Computer Science and Software Engineering Hohai University Jiangsu Nanjing China

ISBN: (纸本)9798400710094

The nested array results in a hole-free difference coarray (DCA) after virtualization, enhancing the utilization of virtual array elements. MUSIC algorithm is an algorithm with high estimation accuracy, but the angle search process has high complexity. In this paper, we propose a spatial smoothing root-MUSIC (SS-root-MUSIC) algorithm based on nested arrays to reduce complexity. Firstly, we obtain the equivalent signals of the DCA and recover the rank through virtualization and spatial smoothing. Then we use the root-MUSIC polynomial to get the DOA estimation results. The proposed SS-root-MUSIC has low computational complexity by avoiding the angle search process while ensuring the estimation accuracy. Theoretical and simulation results confirm the superiority of the proposed SS-root-MUSIC algorithm. © 2024 Copyright held by the owner/author(s).

关键词： Virtualization

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Assessing Secure OpenID-Based EAAA Protocol to Prevent MITM and Phishing Attacks in Web Apps

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computers, Materials & Continua 2023年第6期75卷 4713-4733页

作者： Muhammad Bilal Sandile C.Showngwe Abid Bashir Yazeed Y.Ghadi College of Computer Science and Technology Zhejiang UniversityHangzhou310027China Department of Mathematical Statistics and Actuarial Science Faculty of Natural and Agricultural SciencesUniversity of the Free StateBloemfontein9301South Africa Department of Computer Science National Textile UniversityFaisalabad38000Pakistan Department of Computer Science/Software Engineering Al Ain UniversityAbu DhabiUAE

To secure web applications from Man-In-The-Middle(MITM)and phishing attacks is a challenging task *** this purpose,authen-tication protocol plays a vital role in web communication which securely transfers data from one party to *** authentication works via OpenID,Kerberos,password authentication protocols,***,there are still some limitations present in the reported security *** this paper,the presented anticipated strategy secures both Web-based attacks by leveraging encoded emails and a novel password form pattern *** proposed OpenID-based encrypted Email’s Authentication,Authorization,and Accounting(EAAA)protocol ensure security by relying on the email authenticity and a Special Secret Encrypted Alphanumeric String(SSEAS).This string is deployed on both the relying party and the email server,which is unique and *** first authentication,OpenID Uniform Resource Locator(URL)identity,is performed on the identity provider side.A second authentication is carried out by the hidden Email’s server side and receives a third authentication *** Email’s third SSEAS authentication link manages on the relying party(RP).Compared to existing cryptographic single sign-on protocols,the EAAA protocol ensures that an OpenID URL’s identity is secured from MITM and phishing *** study manages two attacks such as MITM and phishing attacks and gives 339 ms response time which is higher than the already reported methods,such as Single Sign-On(SSO)and *** experimental sites were examined by 72 information technology(IT)specialists,who found that 88.89%of respondents successfully validated the user authorization provided to them via *** proposed EAAA protocol minimizes the higher-level risk of MITM and phishing attacks in an OpenID-based atmosphere.

关键词： Secure user authentication SSO OpenID phishing attack MITM attack

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Transferable Adversarial Facial Images for Privacy Protection 24

Transferable Adversarial Facial Images for Privacy Protectio...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Li, Minghui Wang, Jiangxiong Zhang, Hao Zhou, Ziqi Hu, Shengshan Pei, Xiaobing Software Engineering Huazhong University of Science and Technology Wuhan China Computer Science and Technology Huazhong University of Science and Technology Wuhan China Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798400706868

The success of deep face recognition (FR) systems has raised serious privacy concerns due to their ability to enable unauthorized tracking of users in the digital world. Previous studies proposed introducing imperceptible adversarial noises into face images to deceive those face recognition models, thus achieving the goal of enhancing facial privacy protection. Nevertheless, they heavily rely on user-chosen references to guide the generation of adversarial noises, and cannot simultaneously construct natural and highly transferable adversarial face images in black-box scenarios. In light of this, we present a novel face privacy protection scheme with improved transferability while maintain high visual quality. We propose shaping the entire face space directly instead of exploiting one kind of facial characteristic like makeup information to integrate adversarial noises. To achieve this goal, we first exploit global adversarial latent search to traverse the latent space of the generative model, thereby creating natural adversarial face images with high transferability. We then introduce a key landmark regularization module to preserve the visual identity information. Finally, we investigate the impacts of various kinds of latent spaces and find that F latent space benefits the trade-off between visual naturalness and adversarial transferability. Extensive experiments over two datasets demonstrate that our approach significantly enhances attack transferability while maintaining high visual quality, outperforming state-of-the-art methods by an average 25% improvement in deep FR models and 10% improvement on commercial FR APIs. © 2024 ACM.

关键词： Differential privacy

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Arm PSA-Certified IoT Chip Security: A Case Study

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Tsinghua science and Technology 2023年第2期28卷 244-257页

作者： Fei Chen Duming Luo Jianqiang Li Victor C.M.Leung Shiqi Li Junfeng Fan College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China Department of Electrical and Computer Engineering the University of British ColumbiaVancouverBC V6T 1Z4Canada Open Security Research Inc.Shenzhen 518000China

With the large scale adoption of Internet of Things(IoT)applications in people’s lives and industrial manufacturing processes,IoT security has become an important problem *** security significantly relies on the security of the underlying hardware chip,which often contains critical information,such as encryption *** understand existing IoT chip security,this study analyzes the security of an IoT security chip that has obtained an Arm Platform Security Architecture(PSA)Level 2 *** analysis shows that the chip leaks part of the encryption key and presents a considerable security ***,we use commodity equipment to collect electromagnetic traces of the *** a statistical T-test,we find that the target chip has physical leakage during the AES encryption *** further use correlation analysis to locate the detailed encryption interval in the collected electromagnetic trace for the Advanced Encryption Standard(AES)encryption *** the basis of the intermediate value correlation analysis,we recover half of the 16-byte AES encryption *** repeat the process for three different tests;in all the tests,we obtain the same result,and we recover around 8 bytes of the 16-byte AES encryption ***,experimental results indicate that despite the Arm PSA Level 2 certification,the target security chip still suffers from physical *** layer application developers should impose strong security mechanisms in addition to those of the chip itself to ensure IoT application security.

关键词： Internet of Things(IoT)security chip Arm Platform Security Architecture(PSA)certification electromagnetic side-channel attack Advanced Encryption Standard(AES)encryption key leakage

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Forensic Investigation of Malicious Activities in Digital Environments 4

Forensic Investigation of Malicious Activities in Digital En...

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4th IEEE International Conference on Data engineering and Communication Systems, ICDECS 2024

作者： Pasumpon Pandian, A. Anakath, A.S. Kannadasan, R. Ravikumar, K. Abdul Kareem, D. Care College of Engineering Department of Computer Science and Engineering Trichy India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India School of Computer Science and Engineering Department of Software Systems Tamil Nadu Vellore India Rrase College of Engineering Padappai Department of Computer Science & Engineering Chennai India Grt Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Tamil Nadu Tiruttani India

ISBN: (纸本)9798350393354

Several digital dangers were investigated. Malware dominated analysis with 45 attacks. We found 30 phishing attacks. 22 data breaches, 15 cyber espionage, 18 identity theft. This indicates the kind and frequency of harassment digital forensic specialists endure. Digital evidence was crucial to the case. 65 pieces of evidence were collected on computers. 42 incidents showed smartphones and tablets' digital investigative potential. There were 55 examples of communication patterns requiring network logs. In 28 and 12, cloud services and removable media provided proof. A wide range of evidence sources stresses the need for extensive collecting. Digital forensics were essential to the inquiry. In 70% of cases, disk imaging software created forensic copies of metadata-and timestamp-containing storage media. Network packet analyzers examined 48 traffic logs and communication patterns. To analyze volatile memory, 35 cases used memory forensics. In 25 cases, malware analysis platforms decrypted malware. In 60 situations, file recovery software recovered lost or hidden files. Digital forensic specialists' diverse arsenal for thorough investigations is shown here. Legal and ethical considerations dominated the study. The law was important, and all inquiries followed norms. A strong chain of custody protects evidence integrity and admissibility. The data privacy rules were respected. Legal evidence is admissible in court. Ethics ensured objective investigations. This underlines digital forensic investigations' legal and ethical foundation. © 2024 IEEE.

关键词： Cybersecurity

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Unified Approach Integrating Machine Learning Algorithms for Detection of e-Mail Phishing Attacks 1

Unified Approach Integrating Machine Learning Algorithms for...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Mathur, Abeer Dubey, Abhinav Sudhakar Mahara, Pranav Singh Gandhi, Usha Devi School of Computer Science & Engineering Vellore Institute of Technology Department of Software Systems Vellore India School of Computer Science Engineering & Information Systems Vellore Institute of Technology Department of Information Technology Vellore India

ISBN: (纸本)9798350353648

With the ever-rising risk of phishing attacks, which capitalize on vulnerable human behavior in the contemporary digital space, requires new cybersecurity methods. This literary work contributes to the solution by novel incorporation of three techniques: Support Vector Machine (SVM), Natural Language Processing (NLP) and Random Forest (RF). Utilizing NLP for deep analysis of text in phishing e-mails, SVM for strong categorization and Random Forest's decision making by group helps in improving detection. This combination of language and pattern analysis along with group learning builds a fortified system. This approach differs from prior work on a variety of factors such as reliability, security, integrity, scalability, interpretability, transparency, and robustness. Thorough evaluation on a reliable dataset aimed to demonstrate the method's effectiveness, which resulted in 98.7 % of accuracy in detection. This majorly aims at showcasing the potential of ensemble techniques to combat against cyber-attacks like phishing for laying a strong foundation for a secure digital environment. © 2024 IEEE.

关键词： Phishing

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Density estimation-based method to determine sample size for random sample partition of big data

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Frontiers of computer science 2024年第5期18卷 57-70页

作者： Yulin HE Jiaqi CHEN Jiaxing SHEN Philippe FOURNIER-VIGER Joshua Zhexue HUANG Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen 518107China College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China Department of Computing and Decision Sciences Lingnan UniversityHong Kong 999077China

Random sample partition(RSP)is a newly developed big data representation and management model to deal with big data approximate computation *** research and practical applications have confirmed that RSP is an efficient solution for big data processing and ***,a challenge for implementing RSP is determining an appropriate sample size for RSP data *** a large sample size increases the burden of big data computation,a small size will lead to insufficient distribution information for RSP data *** address this problem,this paper presents a novel density estimation-based method(DEM)to determine the optimal sample size for RSP data ***,a theoretical sample size is calculated based on the multivariate Dvoretzky-Kiefer-Wolfowitz(DKW)inequality by using the fixed-point iteration(FPI)***,a practical sample size is determined by minimizing the validation error of a kernel density estimator(KDE)constructed on RSP data blocks for an increasing sample ***,a series of persuasive experiments are conducted to validate the feasibility,rationality,and effectiveness of *** results show that(1)the iteration function of the FPI method is convergent for calculating the theoretical sample size from the multivariate DKW inequality;(2)the KDE constructed on RSP data blocks with sample size determined by DEM can yield a good approximation of the probability density function(p.d.f);and(3)DEM provides more accurate sample sizes than the existing sample size determination methods from the perspective of *** demonstrates that DEM is a viable approach to deal with the sample size determination problem for big data RSP implementation.

关键词： random sample partition big data sample size Dvoretzky-Kiefer-Wolfowitz inequality kerneldensity estimator probability density function

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