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25th International Conference on Web Information Systems engineering, WISE 2024

作者： Liu, Haibo Yu, Lianjie Si, Yali Liu, Jinglian School of Cyber Security and Computer Hebei University Baoding Baoding China School of Computer Science and Engineering Changshu Institute of Technology Suzhou China

ISBN: (纸本)9789819605699

Graph Convolution Network (GCN) is a potent deep learning methodology and GCN-based graph data augmentation methods excel in recommender systems. The augmented data, as the higher-order collaborative signals, is generated by pre-training the GCN model on raw data, before making the final prediction. Despite their success, three problems have always existed. First, during the pre-training phase, over-smooth and data sparsity have a serious impact on the quality of augmented graph data. Second, the influence degree of higher-order collaborative signals are difficult to control, resulting in unstable embeddings. Last, the output of the pre-trained model is only used to augment the data and is not utilized in subsequent training phases, leading to performance penalty. In this study, we propose an efficient Collaborative Signals Augmentation model based on GCN for Recommendation (CSA4Rec). In the pre-training phase, graph contrastive learning is utilized to generate distinct embeddings to address over-smooth and data sparsity. To manipulate the higher-order collaborative signals effectively, the signals are generated from raw data in the modeling phase and aggregated independently by GCN in the training phase. The Residual-like connection is employed to leverage the output of the pre-trained model to improve the convergence speed in the next training phase. Extensive experiments demonstrate the effectiveness and rationality of CSA4Rec. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Distortion Evaluation of EMP Sensors Using Associated-Hermite Functions

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computers, Materials & Continua 2023年第1期74卷 1093-1105页

作者： Rupo Ma Siping Gao College of Command and Control Engineering Army Engineering UniversityNanjing210007China Department of Computer Information and Cyber Security Jiangsu Police InstituteNanjing210031China Department of Electrical and Computer Engineering National University of Singapore119260Singapore

Electromagnetic pulse(EMP)is a kind of transient electromagnetic phenomenon with short rise time of the leading edge and wide spectrum,which usually disrupts communications and damages electronic equipment and *** is challenging for an EMP sensor to measure a wideband electromagnetic pulse without distortion for the whole ***,analyzing the distortion of EMP measurement is crucial to evaluating the sensor distortion characteristics and correcting the measurement *** fidelity is usually employed to evaluate the distortion of an ***,this metric depends on specific signal waveforms,thus is unsuitable for evaluating and analyzing the distortion of EMP *** this paper,an associated-hermite-function based distortion analysis method including system transfer matrices and distortion rates is proposed,which is general and independent from individual *** system transfer matrix and distortion rate can be straightforwardly calculated by the signal orthogonal transformation coefficients using associated-hermite *** of a sensor *** is then visualized via the system transfer matrix,which is convenient in quantitative analysis of the *** of a current probe,a coaxial pulse voltage probe and a B-field sensor were performed,based on which the feasibility and effectiveness of the proposed distortion analysis method is successfully verified.

关键词： Electromagnetic pulse associated-hermite functions system transfer matrix distortion rate

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Evaluation of Traditional and Modern Inpainting Algorithms for Image Restoration 11

Evaluation of Traditional and Modern Inpainting Algorithms f...

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11th International Conference on Information Technology, computer and Electrical engineering, ICITACEE 2024

作者： Kurniawan, Aditya Thjahyadi, Kirsten Winther Trinita, Vinny Surya School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia School of Computer Science Bina Nusantara University Cyber Security Jakarta Indonesia

ISBN: (纸本)9798350389289

Image restoration remains a critical area of study in digital forensics and computer vision, requiring dependable tools to reconstruct missing or damaged parts of images. Four prominent inpainting algorithms, such as OpenCV's Navier-Stokes, OpenCV's Telea, Laplacian, and bi-Laplacian will be discussed and evaluated for their performances. The experiment is designed with controlled conditions and generating diverse data scenarios, such as removing and writing over images in various degrees of damages. A few changes made to the code script are also reviewed. These methods will be assessed based on the calculation results of several key metrics, which includes Structural Similarity Index Measure (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Histogram Image Similarity. This paper suggests that traditional inpainting algorithms remain fundamental due to their consistency and efficiency. © 2024 IEEE.

关键词： Navier Stokes equations

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security, Trust, and Privacy in cyber-physical Systems (CPS) 2

Security, Trust, and Privacy in Cyber-physical Systems (CPS)

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2nd International Conference on cyber Physical Systems, Power Electronics and Electric Vehicles, ICPEEV 2024

作者： Busari, Wareez A. Bello, Abeeb A. Department of Cyber Security Science Federal University of Technology Niger State Minna Nigeria Department of Electrical and Computer Engineering Mahindra University Hyderabad India

ISBN: (纸本)9798350388664

This paper explores the convergence of physical and computational components in cyber-Physical Systems (CPS). security, trust, and privacy are paramount for reliable and resilient operation of these interconnected systems across diverse sectors like healthcare and smart cities. The paper analyses the complex interplay of these factors, identifying practical challenges and proposing solutions to enhance system robustness. By analysing trends, research gaps, and offering actionable recommendations, the study aims to empower stakeholders in fortifying CPS against evolving cyber threats. It emphasizes the need for comprehensive strategies to mitigate risks, protect sensitive information, and ensure data integrity and system reliability. Through a holistic approach integrating security, trust, and privacy mechanisms, the research contributes to advancing effective security measures in CPS, ultimately fostering a culture of trust and resilience in the digital era. © 2024 IEEE.

关键词： Differential privacy

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Ensemble of deep learning models with Walrus Optimization Algorithm for accurate botnet recognition and classification

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Iran Journal of computer science 2025年 1-18页

作者： Chacko, Ashwathy Anda Edwin, E. Bijolin Thanka, M. Roshni Ebenezer, V. Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Division of Data Science and Cyber Security Karunya Institute of Technology and Sciences Coimbatore India

The rapid growth of Internet of Things (IoT) networks has introduced significant security challenges, with botnet attacks being one of the most prevalent threats. These attacks exploit vulnerabilities in IoT devices, leading to severe disruptions and damage to critical infrastructures. Detecting botnet attacks in IoT environments is challenging due to the large volume of data, the dynamic nature of traffic, and the diverse attack patterns. To address these issues, we propose a novel approach called Walrus Optimized Ensemble Deep Learning for Anomaly-Based Recognition Classifier (WOAEDL-ABRC), which leverages a combination of advanced machine learning techniques for effective botnet detection. The methodology of this research involves four key components: (1) data preprocessing through min–max normalization to scale the features appropriately, (2) feature selection using the social cooperation search algorithm (SCSA) to identify the most informative attributes, (3) an ensemble deep learning model combining convolutional autoencoder (CAE), bidirectional gated recurrent unit (BiGRU), and deep belief network (DBN) for robust anomaly detection, and (4) hyperparameter optimization using the Walrus Optimization Algorithm (WAOA), which fine-tunes the model parameters for optimal performance. This ensemble approach ensures that the model benefits from the strengths of each individual technique while mitigating the weaknesses of others. The dataset used for this research includes network traffic data from IoT environments, consisting of various botnet attack scenarios and normal traffic patterns. The data undergoes extensive preprocessing and feature selection to reduce dimensionality and enhance the model’s performance. The implementation is carried out in Python using TensorFlow for deep learning, with the WAOA applied to optimize hyperparameters. The results demonstrate the effectiveness of the WOAEDL-ABRC in detecting botnet attacks, achieving superior accuracy, precision

关键词： Botnet recognition Deep learning Feature selection Internet of things Walrus Optimization Algorithm

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Unleashing the power of pseudo-code for binary code similarity analysis

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cybersecurity 2023年第2期6卷 44-61页

作者： Weiwei Zhang Zhengzi Xu Yang Xiao Yinxing Xue School of Computer Science and Engineering University of Science and Technology of ChinaHefeiChina School of Computer Science and Engineering Nanyang Technological UniversitySingaporeSingapore Institute of Information Engineering Chinese Academy of SciencesBeijingChina School of Cyber Security University of Chinese Academy of SciencesBeijingChina

Code similarity analysis has become more popular due to its significant applicantions,including vulnerability detection,malware detection,and patch *** the source code of the software is difficult to obtain under most circumstances,binary-level code similarity analysis(BCSA)has been paid much attention *** recent years,many BCSA studies incorporating Al techniques focus on deriving semantic information from binary functions with code representations such as assembly code,intermediate representations,and control flow graphs to measure the ***,due to the impacts of different compilers,architectures,and obfuscations,binaries compiled from the same source code may vary considerably,which becomes the major obstacle for these works to obtain robust *** this paper,we propose a solution,named UPPC(Unleashing the Power of Pseudo-code),which leverages the pseudo-code of binary function as input,to address the binary code similarity analysis challenge,since pseudocode has higher abstraction and is platform-independent compared to binary *** selectively inlines the functions to capture the full function semantics across different compiler optimization levels and uses a deep pyramidal convolutional neural network to obtain the semantic embedding of the *** evaluated UPPC on a data set containing vulnerabilities and a data set including different architectures(X86,ARM),different optimization options(O0-O3),different compilers(GCC,Clang),and four obfuscation *** experimental results show that the accuracy of UPPC in function search is 33.2%higher than that of existing methods.

关键词： Binary code similarity Machine learning Software security Pseudo-code

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Randomization Strategies in Image Steganography Techniques:A Review

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computers, Materials & Continua 2024年第8期80卷 3139-3171页

作者： AFM Zainul Abadin Rossilawati Sulaiman Mohammad Kamrul Hasan Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia(UKM)BangiSelangor43600Malaysia Department of Information and Communication Engineering Pabna University of Science and TechnologyPabna6600Bangladesh

Image steganography is one of the prominent technologies in data hiding *** system performance mostly depends on the embedding *** goal is to embed strictly confidential information into images without causing perceptible changes in the original *** randomization strategies in data embedding techniques may utilize random domains,pixels,or region-of-interest for concealing secrets into a cover image,preventing information from being discovered by an *** implementation of an appropriate embedding technique can achieve a fair balance between embedding capability and stego image imperceptibility,but it is challenging.A systematic approach is used with a standard methodology to carry out this *** review concentrates on the critical examination of several embedding strategies,incorporating experimental results with state-of-the-art methods emphasizing the robustness,security,payload capacity,and visual quality metrics of the stego *** fundamental ideas of steganography are presented in this work,along with a unique viewpoint that sets it apart from previous works by highlighting research gaps,important problems,and ***,it offers a discussion of suggested directions for future study to advance and investigate uncharted territory in image steganography.

关键词： Information hiding image steganography randomized embedding techniques payload capacity imperceptibility

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Privacy-Enhanced Federated Learning Through Homomorphic Encryption With Cloud Federation 22

Privacy-Enhanced Federated Learning Through Homomorphic Encr...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Xie, Qiqi Zhang, Hong Wang, Miao Wu, Wanqing Sun, Zhibo Hebei University School of Cyber Security and Computer Baoding China Drexel University Department of Computer Science Philadelphia United States

ISBN: (纸本)9798331509712

With the exponential growth of data at edge environments, federated learning has emerged. Nevertheless, it encounters persistent challenges in defending against malicious attacks, such as inference attacks and reconstruction attacks. In this paper, we enhance the security of federated learning by integrating cryptographic primitives, including Elliptic Curve Cryptography (ECC) and homomorphic encryption, with cloud federation to protect private user data from being inferred by adversaries using localized models. Specifically, we employ two cloud service providers (CSPs) to manage encrypted model aggregation and global model decryption services separately, which ensures robust privacy protection and provides a strong solution for safeguarding sensitive information. ECC is utilized to encrypt the private key used in homomorphic encryption. Additionally, we partition the ECC private key into multiple sub-secrets and distribute them among the clients to defend against collusion attacks during the decryption process of the global model. Furthermore, we harness the gradient offset between local and global models to design an optimized objective function tailored for clients handling non-Independent and Identically Distributed (non-IID) and unbalanced distributed datasets. Our experimental results show that our method improves accuracy by 1.5% over the baseline, while also ensuring robust privacy protection. © 2024 IEEE.

关键词： Federated learning

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Implementing Three Factors Authentication System (3FA) to Maintain Employee Attendance Record to Enhance security 3

Implementing Three Factors Authentication System (3FA) to Ma...

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3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Johnson, Plezzant Columbus Kant, Shri Kumar, Sandeep Sharda University Department of Computer Science Engineering India Sharda University Center for Cyber Security and Cryptology India

ISBN: (纸本)9798350338607

Today as we all know that maintaining of employee credential have been some major issues around the world. Finding it more difficult to keep track of employees. With this, the company, institution, or government entity does not know who has entered their work-spaces to steal important information from their systems or databases. This serves as a deep wound for any company that does not keep track of its employees. This has been a major issue within so many Africans country, most especially Liberia. where so many companies, institutions, and governmental entities losing huge amounts of data. The means by which they do not track employee records have been with the traditional sign-in method, which has to do with the pen and paper or book sign in and sign out method, which can easily be stolen or damaged. This is one of the methods that employees use to not sign in when they get to work in order to steal data without anyone knowing that they were at work that day. Coming up with a method that will enable strong security and secure maintenance of employees records with less consuming and a strong level of three-factor authentication (3FA) will enable all employees to sign in with their three-level credentials, which have to do with something you know (knowledge), something you have (possession), or something you are (Inherence).The trustworthiness of identification is increased because this data cannot be copied or hacked. Furthermore, Three (3) Factor Authentication attempts to demonstrate, from a broader perspective, why it is more important to review your security procedures in order to prevent unauthorized persons from gaining access to your systems and obtaining data and information that you have not provided them with. Our system is going to allow user signing in with their user credential on your system after entering your username and password which authenticate you into the system, you as an employee to be verified and attendance to be mark you will use your Fa

关键词： Face recognition

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Deploying Hybrid Ensemble Machine Learning Techniques for Effective Cross-Site Scripting(XSS)Attack Detection

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computers, Materials & Continua 2024年第10期81卷 707-748页

作者： Noor Ullah Bacha Songfeng Lu Attiq Ur Rehman Muhammad Idrees Yazeed Yasin Ghadi Tahani Jaser Alahmadi School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan430073China Department of Computer Science and Engineering University of Engineering and TechnologyLahore54000Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain12555Abu Dhabi Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Cross-Site Scripting(XSS)remains a significant threat to web application security,exploiting vulnerabilities to hijack user sessions and steal sensitive *** detection methods often fail to keep pace with the evolving sophistication of cyber *** paper introduces a novel hybrid ensemble learning framework that leverages a combination of advanced machine learning algorithms—Logistic Regression(LR),Support Vector Machines(SVM),eXtreme Gradient Boosting(XGBoost),Categorical Boosting(CatBoost),and Deep Neural Networks(DNN).Utilizing the XSS-Attacks-2021 dataset,which comprises 460 instances across various real-world trafficrelated scenarios,this framework significantly enhances XSS attack *** approach,which includes rigorous feature engineering and model tuning,not only optimizes accuracy but also effectively minimizes false positives(FP)(0.13%)and false negatives(FN)(0.19%).This comprehensive methodology has been rigorously validated,achieving an unprecedented accuracy of 99.87%.The proposed system is scalable and efficient,capable of adapting to the increasing number of web applications and user demands without a decline in *** demonstrates exceptional real-time capabilities,with the ability to detect XSS attacks dynamically,maintaining high accuracy and low latency even under significant ***,despite the computational complexity introduced by the hybrid ensemble approach,strategic use of parallel processing and algorithm tuning ensures that the system remains scalable and performs robustly in real-time *** for easy integration with existing web security systems,our framework supports adaptable Application Programming Interfaces(APIs)and a modular design,facilitating seamless augmentation of current *** innovation represents a significant advancement in cybersecurity,offering a scalable and effective solution for securing modern web applications against evolving threats.

关键词： Cross-site scripting machine learning XSS detection stacking ensemble learning hybrid learning

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