Journal of King Saud University-Computer and Information Sciences最新文献

{"title":"Systematic review of deep learning solutions for malware detection and forensic analysis in IoT","authors":"Siraj Uddin Qureshi ,&nbsp;Jingsha He ,&nbsp;Saima Tunio ,&nbsp;Nafei Zhu ,&nbsp;Ahsan Nazir ,&nbsp;Ahsan Wajahat ,&nbsp;Faheem Ullah ,&nbsp;Abdul Wadud","doi":"10.1016/j.jksuci.2024.102164","DOIUrl":"10.1016/j.jksuci.2024.102164","url":null,"abstract":"<div><p>The swift proliferation of Internet of Things (IoT) devices has presented considerable challenges in maintaining cybersecurity. As IoT ecosystems expand, they increasingly attract malware attacks, necessitating advanced detection and forensic analysis methods. This systematic review explores the application of deep learning techniques for malware detection and forensic analysis within IoT environments. The literature is organized into four distinct categories: IoT Security, Malware Forensics, Deep Learning, and Anti-Forensics. Each group was analyzed individually to identify common methodologies, techniques, and outcomes. Conducted a combined analysis to synthesize the findings across these categories, highlighting overarching trends and insights.This systematic review identifies several research gaps, including the need for comprehensive IoT-specific datasets, the integration of interdisciplinary methods, scalable real-time detection solutions, and advanced countermeasures against anti-forensic techniques. The primary issue addressed is the complexity of IoT malware and the limitations of current forensic methodologies. Through a robust methodological framework, this review synthesizes findings across these categories, highlighting common methodologies and outcomes. Identifying critical areas for future investigation, this review contributes to the advancement of cybersecurity in IoT environments, offering a comprehensive framework to guide future research and practice in developing more robust and effective security solutions.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 8","pages":"Article 102164"},"PeriodicalIF":5.2,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002532/pdfft?md5=c4b7c6c2f5d782febc67d0ed9dc92f16&pid=1-s2.0-S1319157824002532-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142122886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating PCA with deep learning models for stock market Forecasting: An analysis of Turkish stocks markets","authors":"Taner Uçkan","doi":"10.1016/j.jksuci.2024.102162","DOIUrl":"10.1016/j.jksuci.2024.102162","url":null,"abstract":"<div><p>Financial data such as stock prices are rich time series data that contain valuable information for investors and financial professionals. Analysis of such data is critical to understanding market behaviour and predicting future price movements. However, stock price predictions are complex and difficult due to the intense noise, non-linear structures, and high volatility contained in this data. While this situation increases the difficulty of making accurate predictions, it also creates an important area for investors and analysts to identify opportunities in the market. One of the effective methods used in predicting stock prices is technical analysis. Multiple indicators are used to predict stock prices with technical analysis. These indicators formulate past stock price movements in different ways and produce signals such as buy, sell, and hold. In this study, the most frequently used ten different indicators were analyzed with PCA (Principal Component Analysis. This study aims to investigate the integration of PCA and deep learning models into the Turkish stock market using indicator values and to assess the effect of this integration on market prediction performance. The most effective indicators used as input for market prediction were selected with the PCA method, and then 4 different models were created using different deep learning architectures (LSTM, CNN, BiLSTM, GRU). The performance values of the proposed models were evaluated with MSE, MAE, MAPE and R2 measurement metrics. The results obtained show that using the indicators selected by PCA together with deep learning models improves market prediction performance. In particular, it was observed that one of the proposed models, the PCA-LSTM-CNN model, produced very successful results.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 8","pages":"Article 102162"},"PeriodicalIF":5.2,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002519/pdfft?md5=58ab4242a07a2504cdd39efa0bbba182&pid=1-s2.0-S1319157824002519-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142088601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A self-supervised entity alignment framework via attribute correction","authors":"Xin Zhang ,&nbsp;Yu Liu ,&nbsp;Hongkui Wei ,&nbsp;Shimin Shan ,&nbsp;Zhehuan Zhao","doi":"10.1016/j.jksuci.2024.102167","DOIUrl":"10.1016/j.jksuci.2024.102167","url":null,"abstract":"<div><p>Entity alignment (EA), aiming to match entities with the same meaning across different knowledge graphs (KGs), is a critical step in knowledge fusion. Existing EA methods usually encode the multi-aspect features of entities as embeddings and learn to align the embeddings with supervised learning. Although these methods have achieved remarkable results, two issues have not been well addressed. Firstly, these methods require pre-aligned entity pairs to perform EA tasks, limiting their applicability in practice. Secondly, these methods overlook the unique contribution of digital attributes to EA tasks when utilising attribute information to enhance entity features. In this paper, we propose a self-supervised entity alignment framework via attribute correction. Specifically, we first design a highly effective seed pair generator based on multi-aspect features of entities to solve the labour-intensive problem of obtaining pre-aligned entity pairs. Then, a novel alignment mechanism via attribute correction is proposed to address the problem that different types of attributes have different contributions to the EA task. Extensive experiments on real-world datasets with semantic features demonstrate that our framework outperforms state-of-the-art (SOTA) EA tasks.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 8","pages":"Article 102167"},"PeriodicalIF":5.2,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002568/pdfft?md5=cbabc3cd71250bf4b823be664eeec76d&pid=1-s2.0-S1319157824002568-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142076517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abnormal lower limb posture recognition based on spatial gait feature dynamic threshold detection","authors":"Shengrui Zhang,&nbsp;Ling He,&nbsp;Dan Liu,&nbsp;Chuan Jia,&nbsp;Dechao Zhang","doi":"10.1016/j.jksuci.2024.102161","DOIUrl":"10.1016/j.jksuci.2024.102161","url":null,"abstract":"<div><p>Lower limb rehabilitation training often involves the use of assistive standing devices. However, elderly individuals frequently experience reduced exercise effectiveness or suffer muscle injuries when utilizing these devices. The ability to recognize abnormal lower limb postures can significantly enhance training efficiency and minimize the risk of injury. To address this, we propose a model based on dynamic threshold detection of spatial gait features to identify such abnormal postures. A human-assisted standing rehabilitation device platform was developed to build a lower limb gait depth dataset. RGB data is employed for keypoint detection, enabling the establishment of a 3D lower limb posture recognition model that extracts gait, time, spatial features, and keypoints. The predicted joint angles, stride length, and step frequency demonstrate errors of 4 %, 8 %, and 1.3 %, respectively, with an average confidence of 0.95 for 3D key points. We employed the WOA-BP neural network to develop a dynamic threshold algorithm based on gait features and propose a model for recognizing abnormal postures. Compared to other models, our model achieves a 96 % accuracy rate in recognizing abnormal postures, with a recall rate of 83 % and an F1 score of 90 %. ROC curve analysis and AUC values reveal that the WOA-BP algorithm performs farthest from the pure chance line, with the highest AUC value of 0.89, indicating its superior performance over other models. Experimental results demonstrate that this model possesses a strong capability in recognizing abnormal lower limb postures, encouraging patients to correct these postures, thereby reducing muscle injuries and improving exercise effectiveness.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 8","pages":"Article 102161"},"PeriodicalIF":5.2,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002507/pdfft?md5=27cec39130c542af88b8b1f0132833cd&pid=1-s2.0-S1319157824002507-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142088746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A formal specification language and automatic modeling method of asset securitization contract","authors":"Yang Li ,&nbsp;Kai Hu ,&nbsp;Jie Li ,&nbsp;Kaixiang Lu ,&nbsp;Yuan Ai","doi":"10.1016/j.jksuci.2024.102163","DOIUrl":"10.1016/j.jksuci.2024.102163","url":null,"abstract":"<div><p>Asset securitization is an important financial derivative involving complicated asset transfer operations. Therefore, digitizing traditional asset securitization contracts will improve efficiency and facilitate reliability verification. Furthermore, accurate and verifiable requirement description is essential for collaborative development between financial professionals and software engineers. A domain specific language for writing asset securitization contract has been proposed. This solves the problem of difficulty for financial professionals to directly write smart contract by simplifying writing rules. However, due to existing design of the language focused on some simple scenarios, it is insufficient and informal to describe various detailed scenarios. What is more, there are still many reliability issues, such as verifying the correctness of the logical properties of the contract and ensuring the consistency between the contract text and the contract code, within the language in the generation and execution of smart contracts. To overcome the challenges stated above, we extend, simplify and innovate the syntax subset of the domain specific language and name it AS-SC (Asset Securitization – Smart Contract), which can be used by financial professionals to accurately describe requirements. Besides, because formal methods are math-based techniques that describe system properties and can generate programs in a more formal and reliable manner, we propose a semantic consistent code conversion method, named AS2EB, for converting from AS-SC to Event-B, a common and useful formal language. AS2EB method can be used by software engineers to verify requirements. The combination of AS-SC and AS2EB ensures consistency and reliability of the requirements, and reduces the cost of repeated communications and later testing. Taking the credit asset securitization contract as case study, the feasibility and rationality of AS-SC and AS2EB are validated. In addition, by carrying out experiments on three randomly selected real cases in different classic scenarios, we show high-efficiency and reliability of AS2EB method.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 8","pages":"Article 102163"},"PeriodicalIF":5.2,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002520/pdfft?md5=9af49e4b57c4f2d8d674b3287497b478&pid=1-s2.0-S1319157824002520-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142088599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DAW-FA: Domain-aware adaptive weighting with fine-grain attention for unsupervised MRI harmonization","authors":"Linda Delali Fiasam ,&nbsp;Yunbo Rao ,&nbsp;Collins Sey ,&nbsp;Stacy E.B. Aggrey ,&nbsp;Seth Larweh Kodjiku ,&nbsp;Kwame Opuni-Boachie Obour Agyekum ,&nbsp;Annicet Razafindratovolahy ,&nbsp;Isaac Adjei-Mensah ,&nbsp;Chiagoziem Chima Ukwuoma ,&nbsp;Francis Sam","doi":"10.1016/j.jksuci.2024.102157","DOIUrl":"10.1016/j.jksuci.2024.102157","url":null,"abstract":"<div><p>Magnetic resonance (MR) imaging often lacks standardized acquisition protocols across various sites, leading to contrast variations that reduce image quality and hinder automated analysis. MR harmonization improves consistency by integrating data from multiple sources, ensuring reproducible analysis. Recent advances leverage image-to-image translation and disentangled representation learning to decompose anatomical and contrast representations, achieving consistent cross-site harmonization. However, these methods face two significant drawbacks: imbalanced contrast availability during training affects adaptation performance, and insufficient utilization of spatial variability in local anatomical structures limits model adaptability to different sites. To address these challenges, we propose Domain-aware Adaptive Weighting with Fine-Grain Attention (DAW-FA) for Unsupervised MRI Harmonization. DAW-FA incorporates an adaptive weighting mechanism and enhanced self-attention to mitigate MR contrast imbalance during training and account for spatial variability in local anatomical structures. This facilitates robust cross-site harmonization without requiring paired inter-site images. We evaluated DAW-FA on MR datasets with varying scanners and acquisition protocols. Experimental results show DAW-FA outperforms existing methods, with an average increase of 1.92 ± 0.56 in Peak Signal-to-Noise Ratio (PSNR) and 0.023 ± 0.011 in Structural Similarity Index Measure (SSIM). Additionally, we demonstrate DAW-FA’s impact on downstream tasks: Alzheimer’s disease classification and whole-brain segmentation, highlighting its potential clinical relevance.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 7","pages":"Article 102157"},"PeriodicalIF":5.2,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002465/pdfft?md5=3acf98b5530f688283d52f1b4e9b2c0d&pid=1-s2.0-S1319157824002465-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142041103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SARD: Fake news detection based on CLIP contrastive learning and multimodal semantic alignment","authors":"Facheng Yan,&nbsp;Mingshu Zhang,&nbsp;Bin Wei,&nbsp;Kelan Ren,&nbsp;Wen Jiang","doi":"10.1016/j.jksuci.2024.102160","DOIUrl":"10.1016/j.jksuci.2024.102160","url":null,"abstract":"<div><p>The automatic detection of multimodal fake news can be used to effectively identify potential risks in cyberspace. Most of the existing multimodal fake news detection methods focus on fully exploiting textual and visual features in news content, thus neglecting the full utilization of news social context features that play an important role in improving fake news detection. To this end, we propose a new fake news detection method based on CLIP contrastive learning and multimodal semantic alignment (SARD). SARD leverages cutting-edge multimodal learning techniques, such as CLIP, and robust cross-modal contrastive learning methods to integrate features of news-oriented heterogeneous information networks (N-HIN) with multi-level textual and visual features into a unified framework for the first time. This framework not only achieves cross-modal alignment between deep textual and visual features but also considers cross-modal associations and semantic alignments across different modalities. Furthermore, SARD enhances fake news detection by aligning semantic features between news content and N-HIN features, an aspect largely overlooked by existing methods. We test and evaluate SARD on three real-world datasets. Experimental results demonstrate that SARD significantly outperforms the twelve state-of-the-art competitors in fake news detection, with an average improvement of 2.89% in Mac.F1 score and 2.13% in accuracy compared to the leading baseline models across three datasets.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 8","pages":"Article 102160"},"PeriodicalIF":5.2,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002490/pdfft?md5=497eb195281148df13643994f201fe62&pid=1-s2.0-S1319157824002490-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142076508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Parametrization of generalized triangle groups and construction of substitution-box for medical image encryption","authors":"Aqsa Zafar Abbasi ,&nbsp;Ayesha Rafiq ,&nbsp;Lioua Kolsi","doi":"10.1016/j.jksuci.2024.102159","DOIUrl":"10.1016/j.jksuci.2024.102159","url":null,"abstract":"&lt;div&gt;&lt;div&gt;The construction of strong encryption techniques is crucial to meet the increasing demand for secure transmission as well as storage of medical images. A substitution box (S-Box) is an important component of block ciphers and nonlinearity is an important attribute to consider while designing secure S-boxes. As a result, it is required to create new approaches for producing S-boxes with high non-linearity scores. We present a method of parametrization of the generalized triangle group &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mo&gt;〈&lt;/mo&gt;&lt;mi&gt;x&lt;/mi&gt;&lt;mo&gt;,&lt;/mo&gt;&lt;mi&gt;y&lt;/mi&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;mo&gt;|&lt;/mo&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mi&gt;x&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mi&gt;y&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;5&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mi&gt;w&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;k&lt;/mi&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;mo&gt;〉&lt;/mo&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; as linear groups, where &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;w&lt;/mi&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;mi&gt;x&lt;/mi&gt;&lt;mi&gt;y&lt;/mi&gt;&lt;mi&gt;x&lt;/mi&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mi&gt;y&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;mi&gt;x&lt;/mi&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mi&gt;y&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;4&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; which is extended by the parametrization for triangle group &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mo&gt;〈&lt;/mo&gt;&lt;mi&gt;x&lt;/mi&gt;&lt;mo&gt;,&lt;/mo&gt;&lt;mi&gt;y&lt;/mi&gt;&lt;mo&gt;,&lt;/mo&gt;&lt;mi&gt;t&lt;/mi&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;mo&gt;|&lt;/mo&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mi&gt;x&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mi&gt;y&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;5&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mi&gt;t&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mrow&gt;&lt;mo&gt;(&lt;/mo&gt;&lt;mi&gt;x&lt;/mi&gt;&lt;mi&gt;t&lt;/mi&gt;&lt;mo&gt;)&lt;/mo&gt;&lt;/mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mrow&gt;&lt;mo&gt;(&lt;/mo&gt;&lt;mi&gt;y&lt;/mi&gt;&lt;mi&gt;t&lt;/mi&gt;&lt;mo&gt;)&lt;/mo&gt;&lt;/mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mrow&gt;&lt;mo&gt;(&lt;/mo&gt;&lt;mi&gt;x&lt;/mi&gt;&lt;mi&gt;y&lt;/mi&gt;&lt;mo&gt;)&lt;/mo&gt;&lt;/mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;k&lt;/mi&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;mo&gt;〉&lt;/mo&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;. This parametrization is then used for the construction of a highly nonlinear and secure substitution box designed for &lt;span&gt;&lt;math&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;8&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;/math&gt;&lt;/span&gt; elements, tailored specifically for the finite generalized triangle group case with &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;k&lt;/mi&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; for &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;θ&lt;/mi&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;mn&gt;64&lt;/mn&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; which is parameter for all homomorphism from &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;H&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;5&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; to &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;P&lt;/mi&gt;&lt;mi&gt;S&lt;/mi&gt;&lt;mi&gt;L&lt;/mi&gt;&lt;mrow&gt;&lt;mo&gt;(&lt;/mo&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;mo&gt;,&lt;/mo&gt;&lt;mi&gt;q&lt;/mi&gt;&lt;mo&gt;)&lt;/mo&gt;&lt;/mrow&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;, possessing an order of 1200. We rigorously evaluate and analyze various common security indicators associated with the proposed substitution box. The proposed S-box is evaluated for picture encryption using various statistical approaches. Comparative analysis and additional scrutiny reveal promising attributes, affirming its suitability, efficacy, and potential applicability in the domain of medical image encryption. Our S-box achieves the necessary conditions for secu","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 8","pages":"Article 102159"},"PeriodicalIF":5.2,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142319257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Anomalous behavior detection based on optimized graph embedding representation in social networks","authors":"Ling Xing ,&nbsp;Shiyu Li ,&nbsp;Qi Zhang ,&nbsp;Honghai Wu ,&nbsp;Huahong Ma ,&nbsp;Xiaohui Zhang","doi":"10.1016/j.jksuci.2024.102158","DOIUrl":"10.1016/j.jksuci.2024.102158","url":null,"abstract":"<div><p>Anomalous behaviors in social networks can lead to privacy leaks and the spread of false information. In this paper, we propose an anomalous behavior detection method based on optimized graph embedding representation. Specifically, the user behavior logs are first extracted into a social network user behavior temporal knowledge graph, based on which the graph embedding representation method is used to transform the network topology and temporal information in the user behavior knowledge graph into structural embedding vectors and temporal information embedding vectors, and the hybrid attention mechanism is used to merge the two types of vectors to obtain the final entity embedding to complete the prediction and complementation of the temporal knowledge graph of user behavior. We use graph neural networks, which use the temporal information of user behaviors as a time constraint and capture both user behavioral and semantic information. It converts the two parts of information into vectors for concatenation and linear transformation to obtain a comprehensive representation vector of the whole subgraph, as well as joint deep learning models to evaluate abnormal behavior. Finally, we perform experiments on the Yelp dataset to validate that our method achieves a 9.56% improvement in the F1-score.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 7","pages":"Article 102158"},"PeriodicalIF":5.2,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002477/pdfft?md5=05d482d90b47cc00a3f0c9a6ac74bdda&pid=1-s2.0-S1319157824002477-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142048668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient Wear-Leveling-Aware Data Placement for LSM-Tree based key-value store on ZNS SSDs","authors":"Runyu Zhang,&nbsp;Lening Zhou,&nbsp;Mingjie Li,&nbsp;Yunlin Tan,&nbsp;Chaoshu Yang","doi":"10.1016/j.jksuci.2024.102156","DOIUrl":"10.1016/j.jksuci.2024.102156","url":null,"abstract":"<div><p>Emerging Zoned Namespace (ZNS) is a new-style Solid State Drive (SSD) that manages data in a zoned manner, which can achieve higher performance by strictly obeying the sequential write mode in each zone and alleviating the redundant overhead of garbage collections. Unfortunately, flash memory usually has a serious problem with limited program/erase cycles. Meanwhile, inappropriate data placement strategy of storage systems can lead to imbalanced wear among zones, severely reducing the lifespan of ZNS SSDs. In this paper, we propose a Wear-Leveling-Aware Data Placement (WADP) to solve this problem with negligible performance cost. First, WADP employs a wear-aware empty zone allocation algorithm to quantify the resets of zones and choose the less-worn zone for each allocation. Second, to prevent long-term zone occupation of infrequently written data (namely cold data), we propose a wear-leveling cold zone monitoring mechanism to identify cold zones dynamically. Finally, WADP adopts a real-time I/O pressure-aware data migration mechanism to adaptively migrate cold data for achieving wear-leveling among zones. We implement the proposed WADP in ZenFS and evaluate it with widely used workloads. Compared with state-of-the-art solutions, i.e., LIZA and FAR, the experimental results show that WADP can significantly reduce the standard deviation of zone resets while maintaining decent performance.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 7","pages":"Article 102156"},"PeriodicalIF":5.2,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002453/pdfft?md5=b3f5e8288e8205e799d78965f416b571&pid=1-s2.0-S1319157824002453-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142041102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}