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Review of Techniques for Integrating Security in Software Development Lifecycle

computers, Materials & Continua 2025年第1期82卷 139-172页

作者： Hassan Saeed Imran Shafi Jamil Ahmad Adnan Ahmed Khan Tahir Khurshaid Imran Ashraf College of Electrical and Mechanical Engineering National University of Sciences and Technology(NUST)Rawalpindi46604Pakistan Department of Computer Science Abasyn University Islamabad CampusIslamabad45510Pakistan Military College of Signals National University of Sciences and Technology(NUST)Rawalpindi46600Pakistan Department of Electrical Engineering Yeungnam UniversityGyeongsan38541Republic of Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Republic of Korea

Software-related security aspects are a growing and legitimate concern,especially with 5G data available just at our *** conduct research in this field,periodic comparative analysis is needed with the new techniques coming up *** purpose of this study is to review the recent developments in the field of security integration in the software development lifecycle(SDLC)by analyzing the articles published in the last two decades and to propose a way *** review follows Kitchenham’s review *** review has been divided into three main stages including planning,execution,and *** the selected 100 articles,it becomes evident that need of a collaborative approach is necessary for addressing critical software security risks(CSSRs)through effective risk management/estimation *** risks using a numeric scale enables a comprehensive understanding of their severity,facilitating focused resource allocation and mitigation *** a comprehensive understanding of potential vulnerabilities and proactive mitigation efforts facilitated by protection poker,organizations can prioritize resources effectively to ensure the successful outcome of projects and initiatives in today’s dynamic threat *** review reveals that threat analysis and security testing are needed to develop automated tools for the *** estimation of effort required to prioritize potential security risks is a big challenge in software *** accuracy of effort estimation can be further improved by exploring new techniques,particularly those involving deep *** is also imperative to validate these effort estimation methods to ensure all potential security threats are *** challenge is selecting the right model for each specific security *** achieve a comprehensive evaluation,researchers should use well-known benchmark checklists.

关键词： Software development lifecycle systematic literature review critical software security risks national institute of standards and technology DevSecOps open web application security project McGraw’s touch points

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Man Overboard Detection System Using IoT for Navigation Model

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computers, Materials & Continua 2022年第6期71卷 4955-4969页

作者： Hüseyin Gürüler Murat Altun Faheem Khan Taegkeun Whangbo Department of Information Systems Engineering Mugla Sitki Kocman UniversityMuglaTurkey Artificial Intelligence Lab Department of Computer EngineeringGachon UniversityKorea

Security measures and contingency plans have been established in order to ensure human safety especially in the floating elements like ferry,roro,catamaran,frigate,yacht that are the vehicles services for the purpose of logistic and passenger *** this paper,all processes in the event of Man overboard(MOB)are initiated for smart *** MOB the falling person is totally dependent on the person who first saw the falling *** main objective of this paper is to develop a solution to this significant *** a staff member or a passenger does not see the fall into the sea,undesirable situations such as disappearance,injury and death can occur during the period until the absence of the fallen person is *** this paper,a comprehensive and improved solution is provided in terms of personnel and passenger security especially in all the floating elements,in which human resources are intensively involved like passengers,freight,logistics,fishing,business,yacht,leisure and naval *** this case,if the ship’s personnel or passengers fall into the sea in any way,it detected the fallen person into the sea by the sensors in the portable emergency device,which each person will *** warning system is activated via the in-ship automation system to which the information is transmitted by wireless ***,the case of MOB will be determined *** of things(IoT)has a key role in identifying the location and information of the person falling into the sea through sensors,radio frequency,GPS and connected ***,the alarm system on board will be activated and MOB flag(Oscar)will automatically be *** paper enables the Search and rescue(SAR)operations to be initiated and accelerated without losing time through decision-making process.

关键词： SAR internet of things man overboard arduino wireless communication smart transportation

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Reliable and Energy-Efficient Relay Transmission in WBANs with Wireless Power Transfer: Optimal Design with DRL

Reliable and Energy-Efficient Relay Transmission in WBANs wi...

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2024 IEEE Pacific Rim Conference on Communications, computers and Signal Processing, PACRIM 2024

作者： Li, Shuang Xu, Fang Yang, Hong-Chuan University of Victoria Department of Electrical and Computer Engineering VictoriaBC Canada College of Electronic and Information Engineering Southwest University Chongqing China

ISBN: (纸本)9798350362312

Reliable and energy-efficient wireless transmission is critical to the success of future wireless body area networks (WBANs), as the energy storage capacity of biosensors is strictly constrained. In this article, we study the energy efficiency (EE)-maximization problem in a decode-and-forward (DF)-relay transmission system powered by wireless power transfer (WPT). Specifically, in each time slot, an access point (AP) transmits radio frequency (RF) signals to charge a biosensor and a relay, and then the biosensor transmits its collected data to the AP with the assistance of the DF relay. To ensure human safety and maintain transmission reliability, we formulate the EE-maximization problem under peak power constraints while considering transmission errors. Then, we apply a deep reinforcement learning (DRL) algorithm to train an action policy for determining near-optimal transmission parameter values for each transmission session. Through some selected numerical examples, we show that under peak power constraints, our solution can approach the performance obtained by the exhaustive search and exhibits superior performance compared to the traditional iterative algorithm. Therefore, our solution is instrumental in designing safe, reliable, and energy-efficient WBAN systems. © 2024 IEEE.

关键词： Deep reinforcement learning

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Efficient Object Segmentation and Recognition Using Multi-Layer Perceptron Networks

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computers, Materials & Continua 2024年第1期78卷 1381-1398页

作者： Aysha Naseer Nouf Abdullah Almujally Saud S.Alotaibi Abdulwahab Alazeb Jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Information System Department Umm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Engineering Tech University of KoreaGyeonggi-do15073South Korea

Object segmentation and recognition is an imperative area of computer vision andmachine learning that identifies and separates individual objects within an image or video and determines classes or categories based on their *** proposed system presents a distinctive approach to object segmentation and recognition using Artificial Neural Networks(ANNs).The system takes RGB images as input and uses a k-means clustering-based segmentation technique to fragment the intended parts of the images into different regions and label thembased on their ***,two distinct kinds of features are obtained from the segmented images to help identify the objects of *** Artificial Neural Network(ANN)is then used to recognize the objects based on their *** were carried out with three standard datasets,MSRC,MS COCO,and Caltech 101 which are extensively used in object recognition research,to measure the productivity of the suggested *** findings from the experiment support the suggested system’s validity,as it achieved class recognition accuracies of 89%,83%,and 90.30% on the MSRC,MS COCO,and Caltech 101 datasets,respectively.

关键词： K-region fusion segmentation recognition feature extraction artificial neural network computer vision

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Artificial intelligence in physiological characteristics recognition for internet of things authentication

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Digital Communications and Networks 2024年第3期10卷 740-755页

作者： Zhimin Zhang Huansheng Ning Fadi Farha Jianguo Ding Kim-Kwang Raymond Choo School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083China Faculty of Informatics Engineering Aleppo UniversityAleppoSyria Department of Computer Science Blekinge Institute of Technology37179KarlskronaSweden Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)***,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral *** characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in ***,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate ***,we review the literature on the use of AI in physiological characteristics recognition pub-lished after *** use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their *** also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

关键词： Physiological characteristics recognition Artificial intelligence Internet of things Biological-driven authentication

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Multi-food detection using a modified swin-transfomer with recursive feature pyramid network

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Multimedia Tools and Applications 2024年第19期83卷 57731-57757页

作者： Lee, Chao-Yang Khanum, Abida Kumar, Pinninti Praneeth Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Country Taiwan Department of Electrical Engineering National Cheng Kung University No.1 University Road Tainan City701 Taiwan Department of Aeronautical Engineering National Formosa University Yunlin Country Taiwan

Humans need food, and the food detection system is a fascinating research topic and a complex weight loss mechanism. Eating healthy and balanced is crucial. Over the last few years, studies on food-related tasks have progressed, but the existing system deals only with single- and multi-food items by using convolution neural networks (CNN), and the recent development of Transformers in computer-vision has outperformed famous networks like ResNet 50, VGG 16, etc., but Transformer-based methods are limited in food-related tasks. For this issue, we improve Swin Transformer-based method by taking the dominance of transformer and CNN, we propose a novel multi-food detection utilizing a modified Swin-Transfomer and Recursive Feature Pyramid Network called (MFD-MST) and Swin-Transformer with spatial extraction block (STSE) as backbone to recognize multi-food item in images to improve local and structural information of image and RFP as neck to enhance This feature representation recognizes multi-food items. STSE solves transformer positional encoding, and RFP can look at feature map twice, helping the model build powerful representations. A prominent dataset, UEC-FOOD 100, Indian Food 28, UEC-FOOD 256 was widely tested to construct a detection system that can distinguish various objects in food photos and classify them into food categories. Our model’s evaluation measures vary. MFD-MST outperforms Swin-Transformer by 2.7% at AP[0.50] and 3.3%, 1.4% on three food datasets respectively. Test results suggest that our system accurately detects food items. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.

关键词： Image enhancement

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Inverse Scattering of Two-dimensional Circular Cylindrical Conductors by Convolutional Neural Network 4

Inverse Scattering of Two-dimensional Circular Cylindrical C...

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4th IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2024

作者： Chen, Po-Hsiang Hsu, Hsiu-Hui Chiu, Chien-Ching Chien, Wei Yu, Kai-Xu Tamkang University Department of Electrical and Computer Engineering New Taipei City Taiwan Lunghwa University of Science and Technology Department of Computer Information and Network Engineering Taoyuan City Taiwan

ISBN: (纸本)9798350360721

We used a Convolutional Neural Network (CNN) to reconstruct the electromagnetic imaging of a perfect conductor in half-space through the traverse magnetic wave. Since the conventional iterative algorithm is time-consuming, we developed a real-time electromagnetic imaging technique to apply to the CNN to reconstruct the half-space conducting cylinders. The sizes and positions of the cylinders were calculated through the Direct Sampling Method (DSM) in the scattered field. The original ground truth was fed to CNN for training to predict accurate sizes and positions of the shape. The accurate size and position of the shape for the perfect conductor were reconstructed. © 2024 IEEE.

关键词： Convolution

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Video quality enhancement using different enhancement and dehazing techniques

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16607-16635页

作者： Ayoub, Abeer Naeem, Ensherah A. El-Shafai, Walid El-Samie, Fathi E. Abd Hamad, Ehab K. I. El-Rabaie, El-Sayed M. Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Electrical Department Faculty of Technology and Education Suez University Suez43527 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Video in an outdoor environment suffers from the haze problem due to smoke, dust, and other particles in the atmosphere. The quality of video under these atmospheric conditions is clearly/dramatically degraded, leading to poor contrast and information leakage. The proposed algorithm in this paper depends on pre-processing of video frames prior to the dehazing process. Different enhancement techniques are used to remove noise and control the dynamic range, because each frame has some noise due to sensor measurement errors. This noise might be magnified during the haze removal process, if totally neglected. Previous dehazing algorithms enhance the video contrast and reduce frame degradation. We use optimized dehazing algorithms, namely a dual-transmission-map dehazing algorithm, and a recursive Deep Residual Learning (DRL) network, as dehazing tools. We modify the previous dehazing algorithms to generate new algorithms to be suitable not only for visible frames but also for Near Infrared (NIR) frames. In the optimized dehazing algorithms, the haze effect is reduced, according to the hazing model. In the dual-transmission-map dehazing algorithm, the effect of attenuation parameter R and attenuation weight U on the dehazed frames is investigated. A dual-transmission-map algorithm, depending on the Dark Channel Prior (DCP) technique, is studied. We study the effect of a regularization parameter Ω on the visible and NIR dehazed frames without and with enhancement techniques. For the recursive deep residual learning algorithm, we increase the number of iterations in the DRL network from three iterations in the traditional DRL algorithm without denoising techniques to nine iterations, in order to explore the impact of increasing the number of iterations on the output dehazed frames. Since elapsed time grows with the number of iterations, we terminate the operation after the ninth iteration. A comparison between traditional algorithms and the proposed dehazing algorithms is

关键词： Deep learning

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Intelligent wearable vision systems for the visually impaired in Saudi Arabia

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Neural Computing and Applications 2025年第14期37卷 8571-8591页

作者： Talaat, Fatma M. El-Shafai, Walid Soliman, Naglaa F. Algarni, Abeer D. El-Samie, Fathi E. Abd Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Navigating the world with visual impairments presents unique challenges, often limiting independence and safety. This research introduces SafeStride, a novel algorithm designed to empower visually impaired individuals through real-time obstacle detection and navigation assistance. SafeStride functions through five interconnected phases: sensor data acquisition, data preprocessing, obstacle detection, path planning, and feedback and guidance. A combination of ultrasonic sensors, cameras, and inertial measurement units (IMUs) feed comprehensive environmental data into the system, enabling accurate obstacle detection and safe path planning. Evaluating SafeStride's performance involved the MNIST dataset for image recognition and a dedicated indoor object detection dataset. Additionally, comparisons were made against three deep learning models—convolutional neural network (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU). The results showcased SafeStride's high accuracy and effectiveness in both obstacle detection and classification. Beyond its impressive performance, SafeStride represents a significant leap forward in navigation aids for the visually impaired. By offering a comprehensive solution that enhances safety and independence in diverse environments, it holds immense potential to improve the lives of countless individuals. Future efforts will focus on further optimizing the algorithm and testing its real-world capabilities, paving the way for a future where safe and independent navigation becomes a reality for all. All three deep learning models (CNN, LSTM, GRU) excelled in the experiment, achieving high accuracy above 0.98, strong recall and F1-scores near 0.99, and exceptional AUC-ROC scores exceeding 0.9998. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Motion planning

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Grasshopper KUWAHARA and Gradient Boosting Tree for Optimal Features Classifications

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computers, Materials & Continua 2022年第8期72卷 3985-3997页

作者： Rabab Hamed M.Aly Aziza I.Hussein Kamel H.Rahouma The Higher Institute for Management and Information Technology Minya61768Egypt Department of Electrical and Computer Engineering Effat UniversityJeddahKSA Electrical Engineering Department Faculty of EngineeringMinia UniversityMinia6111Egypt

This paper aims to design an optimizer followed by a Kawahara filter for optimal classification and prediction of employees’*** algorithm starts by processing data by a modified K-means technique as a hierarchical clustering method to quickly obtain the best features of employees to reach their best *** work of this paper consists of two *** first part is based on collecting data of employees to calculate and illustrate the performance of each *** second part is based on the classification and prediction techniques of the employee *** model is designed to help companies in their decisions about the employees’*** classification and prediction algorithms use the Gradient Boosting Tree classifier to classify and predict the *** of the paper give the percentage of employees which are expected to leave the company after predicting their performance for the coming *** also show that the Grasshopper Optimization,followed by“KF”with the Gradient Boosting Tree as classifier and predictor,is characterized by a high *** proposed algorithm is compared with other known techniques where our results are fund to be superior.

关键词： Metaheuristic algorithm KUWAHARA filter Grasshopper optimization algorithm and Gradient boosting tree

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