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Proposed Privacy Preservation Technique for Color Medical Images

Intelligent Automation & Soft Computing 2023年第4期36卷 719-732页

作者： Walid El-Shafai Hayam A.Abd El-Hameed Noha A.El-Hag Ashraf A.M.Khalaf Naglaa F.Soliman Hussah Nasser AlEisa Fathi E.Abd El-Samie Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Electrical Communications Engineering Department Faculty of EngineeringMinia UniversityMinia61111Egypt Higher Institute of Commercial Science Management Information SystemsEl-Mahala El-KobraEgypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia

Nowadays,the security of images or information is very *** paper introduces a proposed hybrid watermarking and encryption technique for increasing medical image ***,the secret medical image is encrypted using Advanced Encryption Standard(AES)***,the secret report of the patient is embedded into the encrypted secret medical image with the Least Signiﬁcant Bit(LSB)watermarking *** that,the encrypted secret medical image with the secret report is concealed in a cover medical image,using Kekre’s Median Codebook Generation(KMCG)***,the stego-image obtained is split into 16 ***,it is sent to the *** adopt this strategy to send the secret medical image and report over a network *** proposed technique is assessed with different encryption quality metrics including Peak Signal-to-Noise Ratio(PSNR),Correlation Coefﬁcient(Cr),Fea-ture Similarity Index Metric(FSIM),and Structural Similarity Index Metric(SSIM).Histogram estimation is used to conﬁrm the matching between the secret medical image before and after *** results demonstrate that the proposed technique achieves good performance with high quality of the received medical image and clear image details in a very short processing time.

关键词： LSB steganography AES algorithm KMCG algorithm

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MAD-ANET:Malware Detection Using Attention-Based Deep Neural Networks

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computer Modeling in engineering & Sciences 2025年第4期143卷 1009-1027页

作者： Waleed Khalid Al-Ghanem Emad Ul Haq Qazi Tanveer Zia Muhammad Hamza Faheem Muhammad Imran Iftikhar Ahmad Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11362Saudi Arabia Centre of Artificial Intelligence Naif Arab University for Security SciencesRiyadh14812Saudi Arabia School of Arts and Sciences The University of Notre DameSydneyNSW 2007Australia Center for Smart Analytics Institute of InnovationScience and SustainabilityFederation University AustraliaBerwickVIC 3806Australia Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

In the current digital era,new technologies are becoming an essential part of our ***,the number ofmalicious software ormalware attacks is rapidly *** is no doubt,themajority ofmalware attacks can be detected by most antivirus ***,such types of antivirus programs are one step behind malicious *** to these dilemmas,deep learning become popular in the detection and classification of malicious ***,researchers have significantly focused on finding solutions for malware attacks by analyzing malicious samples with the help of different techniques and *** this research,we presented a lightweight attention-based novel deep Convolutional Neural Network(DNN-CNN)model for binary and multi-class malware classification,including benign,trojan horse,ransomware,and *** applied the Principal Component Analysis(PCA)technique for feature extraction for binary *** used the Synthetic Minority Oversampling Technique(SMOTE)to handle the imbalanced data during multi-class *** proposed attention-based malware detectionmodel is trained on the benchmarkmalware memory dataset named *** indicate that our model obtained high accuracy for binary and multi-class classification,99.5% and 97.9%,respectively.

关键词： Attention-based CNN malware detection machine learning deep learning classification

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ESSM: An extractive summarization model with enhanced spatial-temporal information and span mask encoding 5

ESSM: An extractive summarization model with enhanced spatia...

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5th International Conference on computer Information Science and Application technology, CISAT 2022

作者： Li, Ran Zheng, Fengbo Liang, Gongbo Jiang, Lifen Wu, Panpan Chen, Bowei College of Computer and Information Engineering Tianjin Normal University Tianjin China Department of Computing & Cyber Security Texas A&M University-San Antonio TX78224 United States

ISBN: (纸本)9781510660076

Extractive reading comprehension is to extract consecutive subsequences from a given article to answer the given question. Previous work often adopted Byte Pair Encoding (BPE) that could cause semantically correlated words to be separated. Also, previous features extraction strategy cannot effectively capture the global semantic information. In this paper, an extractive summarization model is proposed with enhanced spatial-temporal information and span mask encoding (ESSM) to promote global semantic information. ESSM utilizes Embedding Layer to reduce semantic segmentation of correlated words, and adopts TemporalConvNet Layer to relief the loss of feature information. The model can also deal with unanswerable questions. To verify the effectiveness of the model, experiments on datasets SQuAD1.1 and SQuAD2.0 are conducted. Our model achieved an EM of 86.31% and a F1 score of 92.49% on SQuAD1.1 and the numbers are 80.54% and 83.27% for SQuAD2.0. It was proved that the model is effective for extractive QA task. © 2022 SPIE.

关键词： Semantics

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Next Generation Image Watermarking via Combined DWT-SVD Technique 2

Next Generation Image Watermarking via Combined DWT-SVD Tech...

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2nd International Conference on cyber Resilience, ICCR 2024

作者： Al-Na'amneh, Qais Almomani, Ammar Nasayreh, Ahmad Nahar, Khalid M.O. Gharaibeh, Hasan Al Mamlook, Rabia Emhamed Alauthman, Mohammad Applied Science Private University Department Of Cyber Security Amman Jordan School Of Computing Skyline University College P.O. Box 1797 Sharjah United Arab Emirates AlHuson University College AlBalqa Applied University I. T. Department Irbid Jordan Yarmouk University Department Of Computer Sciences Irbid211633 Jordan Yarmouk University Faculty Of Information Technology Computer Sciences Irbid211633 Jordan Trine University Dep. Of Business Administration IN United States University Of Zawia Dep.of Mechanical And Industrial Engineering Tripoli Libya University Of Petra Faculty Of Information Technology Department Of Information Security Amman Jordan

ISBN: (纸本)9798350394962

Personal computers and the Internet are used in different areas and are easier to use. Most data is easy to transmit and duplicate in digital format, and being tampered with and stolen easily leads to issues for content owners and service providers. The Paper describes a secure (tampering-resistant) method for watermarking images for protection and authentication and digital media like audio, video, and multimedia data, and a combination of singular value decomposition (SVD) and Discrete wavelet transform (DWT) for insertion watermark by MATLAB to increase accuracy, simplicity, and flexibility Readability, secure robustness, and imperceptibility the visibility of the image. The proposed algorithm consists of three main steps: Firstly, we decomposed the original image into four sub-bands and the original watermark. Secondly, embed the watermark in the LL sub-band by replacing the singular value for the watermark. Finally, embed a watermark in the other three sub-bands by adding LL values for a watermark in each sub-band in the original image. © 2024 IEEE.

关键词： Discrete wavelet transforms

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Membership Inference Attacks via Dynamic Adversarial Perturbations Reduction 23

Membership Inference Attacks via Dynamic Adversarial Perturb...

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23rd IEEE International Conference on Trust, security and Privacy in Computing and Communications, TrustCom 2024

作者： Ding, Zehua Tian, Youliang Wang, Guorong Xiong, Jinbo Ma, Jianfeng Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang China Fujian Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fuzhou China Xidian University School of Cyber Engineering Xian China

Existing membership inference attacks (MIAs) based on adversarial attacks typically introduce excessively large adversarial perturbations to change the model predictions. However, such methods are not only likely to obscure the characteristic memory of the model regarding member data but also blur the subtle characteristic differences between member and non-member data. To address this issue, we propose a novel dynamic adversarial perturbation reduction MIA (DAPR-MIA), which aims to enhance the ability to identify the membership of samples by reducing the perturbation strength. Specifically, DAPR-MIA first conducts a fine-grained analysis of the gradient components of conventional adversarial examples, assessing the impact of each element on the model decision to generate a sensitivity mask. This mask identifies dimensions within adversarial examples that significantly influence model predictions, referred to as sensitive dimensions. Then, by dynamically reducing the perturbation size in these sensitive dimensions, DAPR-MIA gradually approaches the minimum perturbation required to change the prediction result of samples, maximizing the retention of membership features within the sample. Finally, the deviation of the prediction results between the original sample and the adversarial example after perturbation attenuation on the target model is measured to infer whether the sample belongs to a member data. Experimental results show that DAPR-MIA significantly outperforms existing methods on multiple datasets and deep models, demonstrating state-of-the-art attack accuracy. © 2024 IEEE.

关键词： Perturbation techniques

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Cluster Availability Model with Information Update Following Node Recovery

Cluster Availability Model with Information Update Following...

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2023 Wave Electronics and its Application in Information and Telecommunication Systems, WECONF 2023

作者： Bogatyrev, V.A. Bogatyrev, S.V. Sivov, V.V. Saint-Petersburg State University of Aerospace Instrumentation Department Information Systems Security Saint Petersburg Russia ITMO University Faculty of Software Engineering and Computer Technology Saint-Petersburg Russia

ISBN: (纸本)9798350348293

A model for evaluating the availability of a fault-tolerant cluster with a constraint on the maximum query service time is proposed. Node recovery in the cluster involves replacing the entire computational node with the latest backup results before the failure. Information update after the backup is achieved through the replication of this information from the working nodes of the cluster. The purpose of the article is to assess the reliability of the cluster and its readiness to timely service requests critical to the waiting time. Restoring up-to-date data in a cluster node after previously entering the data saved during backup into it requires the use of resources of the node in which the data is being restored and the node containing up-to-date data. The specified pair of nodes is excluded from the request servicing process, which reduces the likelihood of timely maintenance. Information recovery in case of failures of all nodes containing replicas of up-to-date data requires more time compared to replication from the memory of a functioning node. © 2023 IEEE.

关键词： Fault tolerance

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Sector-Independent Integrated System Architecture for Profiling Hazardous Industrial Wastes

Sector-Independent Integrated System Architecture for Profil...

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作者： Nwankwo, Wilson Adetunji, Charles O. Ukhurebor, Kingsley E. David, Acheme I. Makinde, Samuel Nwankwo, Chukwuemeka P. Umezuruike, Chinecherem Department of Cyber Security Delta State University of Science and Technology Ozoro Nigeria Department of Computer Science Edo State University Uzairue Nigeria Department of Microbiology Edo State University Uzairue Nigeria Department of Physics Edo State University Uzairue Nigeria College of Computing Bowen University Iwo Nigeria

The accummulation of waste in urban societies has been christened the "industrialization and urbanization aftermath" in some quarters. In recent times, the climate change phenomenon is attributed to the generation and release of massive wastes from humanity’s economic activities globally. The move to restore the ecological integrity of the earth’s habitat is paramount and many programmes and policies are currently being designed to solve this global challenge. This paper reviews the current industrial waste management approaches and practices including legislation, machineries, and policies in Lagos Nigeria, a major urban area and commercial centre in sub-Saharan Africa. The study adopts a narrative-expository posture with enquiries made in respect of all operations and machineries of waste management in the urban and surrounding areas. Findings show that while machineries and policies are substantially crafted to meet the changing socio-economic developments, there is a gap in the deployment of modern information technologies in fostering the policy provisions in such a manner that complements the efforts of the relevant government machineries while ensuring sustainable development. The current waste management practices do not reflect what is considered the minimum global best practices. Accordingly, this study proposes a technology-driven architecture that employs an intelligent integrated resource planning system to support collective and collaborative management of industrial wastes particularly in the area of risk management, profiling, and resource allocation. The proposed architecture is significant in that it provides a realistic coordinating single window to harness all relevant existing practices through the introduction of a computerized platform that includes risk and hazard profiling of various industry subsectors, industry operators, location of operation, citizen reporting, environmental impact, feedback, etc. Thus, this architecture promotes collect

关键词： Sustainable development

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A Novel Machine Learning-Based Framework for Predicting Diabetes in Healthcare

A Novel Machine Learning-Based Framework for Predicting Diab...

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Intelligent Control, Computing and Communications (IC3), International Conference on

作者： A. Sheryl Oliver Kimmi Gupta Jagendra Singh Atul Kumar Agnihotri M. Vasavi Gaganjot Kaur Department of Computational Intelligence SRM Institute of Science and Technology Chennai India School of Computer Science Engineering & Technology Bennett University Greater Noida India Electronics and Communication Engineering School of Engineering and Technology Chhatrapati Shahu Ji Maharaj University Kanpur India Department of ET -Cyber Security CVR College of Engineering Hyderabad India Department of Computer Science Raj Kumar Goel Institute of Technology Ghaziabad India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

The rise in diabetes is considered the greatest global health issue; thus, the call for early and accurate predictions of the disease is a dire need in the health sector. The detection of the disease in its initial stages would allow patients to undergo timely interventions and appropriate treatments. A novel approach for the prediction of stages and severity of diabetes by utilizing various models within a machine learning framework, such as Support Vector Machine (SVM), Artificial Neural Network (ANN), Decision tree learning (DT), and Random Forest (RF) is proposed in this paper. The training data would involve the history of medical conditions, age, BMI, insulin, skin thickness, glucose level, the number of pregnancies, and existence or non-existence of diabetes. The models were trained on 70% of the data and tested on the remaining 30%. The results show that SVM outperforms all other models, achieving an accuracy of 96.5%, followed by ANN with 94.5%, DT with 90.2 %, and RF with 86.7%. These results indicate that SVM, followed by ANN, is the most effective model for predicting diabetes stages. The accuracy and reduction of data loss have been constant improvements with epochs 30 to 360; therefore, models can be used in real-time applications in the clinical area. Thus, this research concludes that machine learning models, such as SVM, may be helpful in predicting and diagnosing early cases of diabetes; thus, these models could be helpful in making good decisions on healthcare and improving the patient's health.

关键词： Support vector machines Radio frequency Accuracy Computational modeling Artificial neural networks Predictive models Data models Real-time systems Diabetes Random forests

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cybersecurity Threats in the Era of AI: Detection of Phishing Domains Through Classification Rules 2

Cybersecurity Threats in the Era of AI: Detection of Phishin...

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2nd International engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Aljaidi, Mohammad Alsarhan, Ayoub Al-Fraihat, Dimah Al-Arjan, Ahmed Igried, Bashar El-Salhi, Subhieh M. Khalid, Muhammad Al-Na'amneh, Qais Zarqa University Faculty of Information Technology Department of Computer Science Zarqa13110 Jordan The Hashemite University Department of Information Technology Jordan Isra University Faculty of Information Technology Department of Software Engineering Amman11622 Jordan The Hashemite University Faculty of Prince AL-Hussein Bin Abdullah Ii of Information Technology Department of Information Technology Jordan The Hashemite University Department of Computer Science and Applications Zarqa Jordan The Hashemite University P. O Box 330127 Zarqa13133 Jordan University of Hull Department of Computer Sciences and Technology Kingston upon Hull United Kingdom Applied Science Private University Dep. of Cyber Security Amman Jordan

ISBN: (纸本)9798350373363

Phishing is a cyber threat where attackers create deceptive websites or emails to exploit individuals for fraudulent purposes such as providing sensitive information like usernames, passwords, or financial details. Hence, detecting phishing emails is of paramount importance to protect sensitive information, preventing financial losses, protecting against identity theft, and maintaining trust. This study employs machine learning techniques to classify and categorize websites that are used for phishing attacks. To achieve this, we proposed a classification model for the detection of phishing domains based on classification rules. These rules were formulated based on analyzing and distributing them into three classifiers: domain name features, search engine opinion (SEO) information of the domain, and security scans by security vendors. A total of eleven classification rules were formulated which were distributed as follows: six rules for the first classifier, four rules for the second classifier, and one rule for the third classifier. Each classifier feeds its results to the main classifier, which then evaluates the received results based on its rules. Domain names from 222 confirmed phishing URLs were used to test the suggested model. The model demonstrated exceptional performance, achieving an impressive accuracy rate of 99.1% during testing. © 2023 IEEE.

关键词： Websites

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Membership Inference Attacks via Dynamic Adversarial Perturbations Reduction

Membership Inference Attacks via Dynamic Adversarial Perturb...

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

作者： Zehua Ding Youliang Tian Guorong Wang Jinbo Xiong Jianfeng Ma State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fujian Normal University Fuzhou China School of Cyber Engineering Xidian University Xian China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

关键词： Privacy Sensitivity Perturbation methods Computational modeling Predictive models Attenuation measurement Attenuation Data models Robustness security

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