Journal of King Saud University-Computer and Information Sciences最新文献_第6页

Real-time segmentation and classification of whole-slide images for tumor biomarker scoring 用于肿瘤生物标记物评分的全切片图像实时分割和分类

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-11-01 Epub Date: 2024-10-05 DOI: 10.1016/j.jksuci.2024.102204

Md Jahid Hasan , Wan Siti Halimatul Munirah Wan Ahmad , Mohammad Faizal Ahmad Fauzi , Jenny Tung Hiong Lee , See Yee Khor , Lai Meng Looi , Fazly Salleh Abas , Afzan Adam , Elaine Wan Ling Chan

{"title":"Real-time segmentation and classification of whole-slide images for tumor biomarker scoring","authors":"Md Jahid Hasan , Wan Siti Halimatul Munirah Wan Ahmad , Mohammad Faizal Ahmad Fauzi , Jenny Tung Hiong Lee , See Yee Khor , Lai Meng Looi , Fazly Salleh Abas , Afzan Adam , Elaine Wan Ling Chan","doi":"10.1016/j.jksuci.2024.102204","DOIUrl":"10.1016/j.jksuci.2024.102204","url":null,"abstract":"<div><div>Histopathology image segmentation and classification are essential for diagnosing and treating breast cancer. This study introduced a highly accurate segmentation and classification for histopathology images using a single architecture. We utilized the famous segmentation architectures, SegNet and U-Net, and modified the decoder to attach ResNet, VGG and DenseNet to perform classification tasks. These hybrid models are integrated with Stardist as the backbone, and implemented in a real-time pathologist workflow with a graphical user interface. These models were trained and tested offline using the ER-IHC-stained private and H&E-stained public datasets (MoNuSeg). For real-time evaluation, the proposed model was evaluated using PR-IHC-stained glass slides. It achieved the highest segmentation pixel-based F1-score of 0.902 and 0.903 for private and public datasets respectively, and a classification-based F1-score of 0.833 for private dataset. The experiment shows the robustness of our method where a model trained on ER-IHC dataset able to perform well on real-time microscopy of PR-IHC slides on both 20x and 40x magnification. This will help the pathologists with a quick decision-making process.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 9","pages":"Article 102204"},"PeriodicalIF":5.2,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529768","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}

引用次数: 0

Quantum computing enhanced knowledge tracing: Personalized KT research for mitigating data sparsity 量子计算增强知识追踪：缓解数据稀疏性的个性化 KT 研究

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-11-01 Epub Date: 2024-10-24 DOI: 10.1016/j.jksuci.2024.102224

Chengke Bao , Qianxi Wu , Weidong Ji , Min Wang , Haoyu Wang

{"title":"Quantum computing enhanced knowledge tracing: Personalized KT research for mitigating data sparsity","authors":"Chengke Bao , Qianxi Wu , Weidong Ji , Min Wang , Haoyu Wang","doi":"10.1016/j.jksuci.2024.102224","DOIUrl":"10.1016/j.jksuci.2024.102224","url":null,"abstract":"<div><div>With the development of artificial intelligence in education, knowledge tracing (KT) has become a current research hotspot and is the key to the success of personalized instruction. However, data sparsity remains a significant challenge in the KT domain. To address this challenge, this paper applies quantum computing (QC) technology to KT for the first time. It proposes two personalized KT models incorporating quantum mechanics (QM): quantum convolutional enhanced knowledge tracing (QCE-KT) and quantum variational enhanced knowledge tracing (QVE-KT). Through quantum superposition and entanglement properties, QCE-KT and QVE-KT effectively alleviate the data sparsity problem in the KT domain through quantum convolutional layers and variational quantum circuits, respectively, and significantly improve the quality of the representation and prediction accuracy of students’ knowledge states. Experiments on three datasets show that our models outperform ten benchmark models. On the most sparse dataset, QCE-KT and QVE-KT improve their performance by 16.44% and 14.78%, respectively, compared to DKT. Although QC is still in the developmental stage, this study reveals the great potential of QM in personalized KT, which provides new perspectives for solving personalized instruction problems and opens up new directions for applying QC in education.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 9","pages":"Article 102224"},"PeriodicalIF":5.2,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553296","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}

引用次数: 0

DA-Net: A classification-guided network for dental anomaly detection from dental and maxillofacial images DA-Net：从牙科和颌面部图像中检测牙科异常的分类指导网络

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-11-01 Epub Date: 2024-10-30 DOI: 10.1016/j.jksuci.2024.102229

Jiaxing Li

{"title":"DA-Net: A classification-guided network for dental anomaly detection from dental and maxillofacial images","authors":"Jiaxing Li","doi":"10.1016/j.jksuci.2024.102229","DOIUrl":"10.1016/j.jksuci.2024.102229","url":null,"abstract":"<div><div>Dental abnormalities (DA) are frequent signs of disorders of the mouth that cause discomfort, infection, and loss of teeth. Early and reasonably priced treatment may be possible if defective teeth in the oral cavity are automatically detected. Several research works have endeavored to create a potent deep learning model capable of identifying DA from pictures. However, because of the following problems, aberrant teeth from the oral cavity are difficult to detect: 1) Normal teeth and crowded dentition frequently overlap; 2) The lesion area on the tooth surface is tiny. This paper proposes a professional dental anomaly detection network (DA-Net) to address such issues. First, a multi-scale dense connection module (MSDC) is designed to distinguish crowded teeth from normal teeth by learning multi-scale spatial information of dentition. Then, a pixel differential convolution (PDC) module is designed to perform pathological tooth recognition by extracting small lesion features. Finally, a multi-stage convolutional attention module (MSCA) is developed to integrate spatial information and channel information to obtain abnormal teeth in small areas. Experiments on benchmarks show that DA-Net performs well in dental anomaly detection and can further assist doctors in making treatment plans. Specifically, the DA-Net method performs best on multiple detection evaluation metrics: IoU, PRE, REC, and mAP. In terms of REC and mAP indicators, the proposed DA-Net method is 1.1% and 1.3% higher than the second-ranked YOLOv7 method.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 9","pages":"Article 102229"},"PeriodicalIF":5.2,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578584","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}

引用次数: 0

Enhanced UrduAspectNet: Leveraging Biaffine Attention for superior Aspect-Based Sentiment Analysis 增强型 UrduAspectNet：利用双峰注意力实现卓越的基于方面的情感分析

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-11-01 Epub Date: 2024-10-23 DOI: 10.1016/j.jksuci.2024.102221

Kamran Aziz , Naveed Ahmed , Hassan Jalil Hadi , Aizihaierjiang Yusufu , Mohammaed Ali Alshara , Yasir Javed , Donghong Ji

{"title":"Enhanced UrduAspectNet: Leveraging Biaffine Attention for superior Aspect-Based Sentiment Analysis","authors":"Kamran Aziz , Naveed Ahmed , Hassan Jalil Hadi , Aizihaierjiang Yusufu , Mohammaed Ali Alshara , Yasir Javed , Donghong Ji","doi":"10.1016/j.jksuci.2024.102221","DOIUrl":"10.1016/j.jksuci.2024.102221","url":null,"abstract":"<div><div>Urdu, with its rich linguistic complexity, poses significant challenges for computational sentiment analysis. This study presents an enhanced version of UrduAspectNet, specifically designed for Aspect-Based Sentiment Analysis (ABSA) in Urdu. We introduce key innovations including the incorporation of Biaffine Attention into the model architecture, which synergizes XLM-R embeddings, a bidirectional LSTM (BiLSTM), and dual Graph Convolutional Networks (GCNs). Additionally, we utilize dependency parsing to create the adjacency matrix for the GCNs, capturing syntactic dependencies to enhance relational representation. The improved model, termed Enhanced UrduAspectNet, integrates POS and lemma embeddings, processed through BiLSTM and GCN layers, with Biaffine Attention enhancing the extraction of intricate aspect and sentiment relationships. We also introduce the use of BIO tags for aspect term identification, improving the granularity of aspect extraction. Experimental results demonstrate significant improvements in both aspect extraction and sentiment classification accuracy. This research advances Urdu sentiment analysis and sets a precedent for leveraging sophisticated NLP techniques in underrepresented languages.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 9","pages":"Article 102221"},"PeriodicalIF":5.2,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529770","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}

引用次数: 0

Dual-stream dynamic graph structure network for document-level relation extraction 用于文档级关系提取的双流动态图结构网络

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-11-01 Epub Date: 2024-10-03 DOI: 10.1016/j.jksuci.2024.102202

Yu Zhong, Bo Shen

{"title":"Dual-stream dynamic graph structure network for document-level relation extraction","authors":"Yu Zhong, Bo Shen","doi":"10.1016/j.jksuci.2024.102202","DOIUrl":"10.1016/j.jksuci.2024.102202","url":null,"abstract":"<div><div>Extracting structured information from unstructured text is crucial for knowledge management and utilization, which is the goal of document-level relation extraction. Existing graph-based methods face issues with information confusion and integration, limiting the reasoning capabilities of the model. To tackle this problem, a dual-stream dynamic graph structural network is proposed to model documents from various perspectives. Leveraging the richness of document information, a static document heterogeneous graph is constructed. A dynamic heterogeneous document graph is then induced based on this foundation to facilitate global information aggregation for entity representation learning. Additionally, the static document graph is decomposed into multi-level static semantic graphs, and multi-layer dynamic semantic graphs are further induced, explicitly segregating information from different levels. Information from different streams is effectively integrated via an information integrator. To mitigate the interference of noise during the reasoning process, a noise regularization mechanism is also designed. The experimental results on three extensively utilized publicly accessible datasets for document-level relation extraction demonstrate that our model achieves F1 scores of 62.56%, 71.1%, and 86.9% on the DocRED, CDR, and GDA datasets, respectively, significantly outperforming the baselines. Further analysis also demonstrates the effectiveness of the model in multi-entity scenarios.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 9","pages":"Article 102202"},"PeriodicalIF":5.2,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424440","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}

引用次数: 0

The evolution of the flip-it game in cybersecurity: Insights from the past to the future 网络安全翻转游戏的演变：从过去到未来的启示

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-11-01 Epub Date: 2024-09-25 DOI: 10.1016/j.jksuci.2024.102195

Mousa Tayseer Jafar , Lu-Xing Yang , Gang Li , Xiaofan Yang

{"title":"The evolution of the flip-it game in cybersecurity: Insights from the past to the future","authors":"Mousa Tayseer Jafar , Lu-Xing Yang , Gang Li , Xiaofan Yang","doi":"10.1016/j.jksuci.2024.102195","DOIUrl":"10.1016/j.jksuci.2024.102195","url":null,"abstract":"<div><div>Cybercrime statistics highlight the severe and growing impact of digital threats on individuals and organizations, with financial losses escalating rapidly. As cybersecurity becomes a central challenge, several modern cyber defense strategies prove insufficient for effectively countering the threats posed by sophisticated attackers. Despite advancements in cybersecurity, many existing frameworks often lack the capacity to address the evolving tactics of adept adversaries. With cyber threats growing in sophistication and diversity, there is a growing acknowledgment of the shortcomings within current defense strategies, underscoring the need for more robust and innovative solutions. To develop resilient cyber defense strategies, it remains essential to simulate the dynamic interaction between sophisticated attackers and system defenders. Such simulations enable organizations to anticipate and effectively counter emerging threats. The Flip-It game is recognized as an intelligent simulation game for capturing the dynamic interplay between sophisticated attackers and system defenders. It provides the capability to emulate intricate cyber scenarios, allowing organizations to assess their defensive capabilities against evolving threats, analyze vulnerabilities, and improve their response strategies by simulating real-world cyber scenarios. This paper provides a comprehensive analysis of the Flip-It game in the context of cybersecurity, tracing its development from inception to future prospects. It highlights significant contributions and identifies potential future research avenues for scholars in the field. This study aims to deliver a thorough understanding of the Flip-It game’s progression, serving as a valuable resource for researchers and practitioners involved in cybersecurity strategy and defense mechanisms.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 9","pages":"Article 102195"},"PeriodicalIF":5.2,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424356","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}

引用次数: 0

A truthful randomized mechanism for task allocation with multi-attributes in mobile edge computing 移动边缘计算中多属性任务分配的真实随机机制

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-11-01 Epub Date: 2024-09-26 DOI: 10.1016/j.jksuci.2024.102196

Xi Liu , Jun Liu

{"title":"A truthful randomized mechanism for task allocation with multi-attributes in mobile edge computing","authors":"Xi Liu , Jun Liu","doi":"10.1016/j.jksuci.2024.102196","DOIUrl":"10.1016/j.jksuci.2024.102196","url":null,"abstract":"<div><div>Mobile Edge Computing (MEC) aims at decreasing the response time and energy consumption of running mobile applications by offloading the tasks of mobile devices (MDs) to the MEC servers located at the edge of the network. The demands are multi-attribute, where the distances between MDs and access points lead to differences in required resources and transmission energy consumption. Unfortunately, the existing works have not considered both task allocation and energy consumption problems. Motivated by this, this paper considers the problem of task allocation with multi-attributes, where the problem consists of the winner determination and offloading decision problems. First, the problem is formulated as the auction-based model to provide flexible service. Then, a randomized mechanism is designed and is truthful in expectation. This drives the system into an equilibrium where no MD has incentives to increase the utility by declaring an untrue value. In addition, an approximation algorithm is proposed to minimize remote energy consumption and is a polynomial-time approximation scheme. Therefore, it achieves a tradeoff between optimality loss and time complexity. Simulation results reveal that the proposed mechanism gets the near-optimal allocation. Furthermore, compared with the baseline methods, the proposed mechanism can effectively increase social welfare and bring higher revenue to edge server providers.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 9","pages":"Article 102196"},"PeriodicalIF":5.2,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424437","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}

引用次数: 0

Firefly forest: A swarm iteration-free swarm intelligence clustering algorithm 萤火虫森林无迭代群集智能聚类算法

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-11-01 Epub Date: 2024-10-18 DOI: 10.1016/j.jksuci.2024.102219

Shijie Zeng , Yuefei Wang , Yukun Wen , Xi Yu , Binxiong Li , Zixu Wang

{"title":"Firefly forest: A swarm iteration-free swarm intelligence clustering algorithm","authors":"Shijie Zeng , Yuefei Wang , Yukun Wen , Xi Yu , Binxiong Li , Zixu Wang","doi":"10.1016/j.jksuci.2024.102219","DOIUrl":"10.1016/j.jksuci.2024.102219","url":null,"abstract":"<div><div>The Firefly Forest algorithm is a novel bio-inspired clustering method designed to address key challenges in traditional clustering techniques, such as the need to set a fixed number of neighbors, predefine cluster numbers, and rely on computationally intensive swarm iterative processes. The algorithm begins by using an adaptive neighbor estimation, refined to filter outliers, to determine the brightness of each firefly. This brightness guides the formation of firefly trees, which are then merged into cohesive firefly forests, completing the clustering process. This approach allows the algorithm to dynamically capture both local and global patterns, eliminate the need for predefined cluster numbers, and operate with low computational complexity. Experiments involving 14 established clustering algorithms across 19 diverse datasets, using 8 evaluative metrics, demonstrate the Firefly Forest algorithm’s superior accuracy and robustness. These results highlight its potential as a powerful tool for real-world clustering applications. Our code is available at: https://github.com/firesaku/FireflyForest.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 9","pages":"Article 102219"},"PeriodicalIF":5.2,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529769","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}

引用次数: 0

Software requirement engineering over the federated environment in distributed software development process 分布式软件开发过程中联合环境下的软件需求工程

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-11-01 Epub Date: 2024-09-28 DOI: 10.1016/j.jksuci.2024.102201

Abdulaziz Alhumam, Shakeel Ahmed

{"title":"Software requirement engineering over the federated environment in distributed software development process","authors":"Abdulaziz Alhumam, Shakeel Ahmed","doi":"10.1016/j.jksuci.2024.102201","DOIUrl":"10.1016/j.jksuci.2024.102201","url":null,"abstract":"<div><div>In the recent past, the distributed software development (DSD) process has become increasingly prevalent with the rapid evolution of the software development process. This transformation would necessitate a robust framework for software requirement engineering (SRE) to work in federated environments. Using the federated environment, multiple independent softwareentities wouldwork together to develop software, often across organizationsand geographical borders. The decentralized structure of the federated architecture makes requirement elicitation, analysis, specification, validation, and administration more effective.The proposed model emphasizes flexibility and agility, leveraging the collaboration of multiple localized models within a diversified development framework. This collaborative approach is designed to integrate the strengths of each local process, ultimately resulting in the creation of a robust software prototype. The performance of the proposed DSD model is evaluated using two case studies on the E-Commerce website and the Learning Management system. The proposed model is analyzed by considering divergent functional and non-functional requirements for each of the case studies and analyzing the performance using standardized metrics like mean square error (MSE), mean absolute error (MAE), and Pearson Correlation Coefficient (PCC). It is observed that the proposed model exhibited a reasonable performance with an MSE value of 0.12 and 0.153 for both functional and non-functional requirements, respectively, and an MAE value of 0.222 and 0.232 for both functional and non-functional requirements, respectively.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 9","pages":"Article 102201"},"PeriodicalIF":5.2,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424357","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}

引用次数: 0

Systematic review of deep learning solutions for malware detection and forensic analysis in IoT 对用于物联网恶意软件检测和取证分析的深度学习解决方案进行系统审查

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-10-01 Epub Date: 2024-08-27 DOI: 10.1016/j.jksuci.2024.102164

Siraj Uddin Qureshi , Jingsha He , Saima Tunio , Nafei Zhu , Ahsan Nazir , Ahsan Wajahat , Faheem Ullah , Abdul Wadud

{"title":"Systematic review of deep learning solutions for malware detection and forensic analysis in IoT","authors":"Siraj Uddin Qureshi , Jingsha He , Saima Tunio , Nafei Zhu , Ahsan Nazir , Ahsan Wajahat , Faheem Ullah , 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":"2024-10-01","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}

引用次数: 0