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Ransomware-as-a-service (RaaS) lowers barriers for attackers, and generative AI may enable new offender capabilities, creating urgent demands for defensive mechanisms that are both accurate and interpretable. The study proposes TTMCDA-XAIBDL, a Two-Tier metaheuristic approach using feature selection, Bayesian neural network classification, hyperparameter optimization, and SHAP-based explainable AI. Simulations on a ransomware detection dataset yield 99.29% accuracy, outperforming recent methods.",{"@graph":69,"@context":126},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/two-tier-heuristic-search-for-ransomware-as-a-service-based-cyberattack-defence-analysis-using-explainable-bayesian-deep-learning-model/450448/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/two-tier-heuristic-search-for-ransomware-as-a-service-based-cyberattack-defence-analysis-using-explainable-bayesian-deep-learning-model/450448.png","ImageObject",300,407,{"name":92,"@type":93},"\tCallum ","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-06","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":81},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118,122],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the study address in ransomware-as-a-service attacks?","Question",{"text":112,"@type":113},"It targets the growing threat of RaaS-based cyberattacks and the need for progressive defensive mechanisms that are understandable rather than opaque black-box models.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does TTMCDA-XAIBDL build its detection pipeline?",{"text":117,"@type":113},"It applies Z-score normalization for preprocessing, uses improved sand cat swarm optimization for feature selection, classifies using a Bayesian neural network, and tunes hyperparameters with whale optimization.",{"name":119,"@type":110,"acceptedAnswer":120},"How is explainability provided in the proposed method?",{"text":121,"@type":113},"SHAP is integrated as explainable artificial intelligence to reveal insights into the model’s decision-making process and support trust in the system.",{"name":123,"@type":110,"acceptedAnswer":124},"What performance result is reported for the proposed approach?",{"text":125,"@type":113},"Experiments on a ransomware detection dataset show a superior accuracy of 99.29% compared with recent methods.","https://schema.org",{"og:url":83,"og:type":128,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":130,"canonical":83},"index,follow",{"doc_id":132,"site_id":62},450448,1791123479,{"code":4,"msg":5,"data":135},{"doc_id":132,"user_id":136,"nickname":92,"user_avatar":137,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":138,"file_id":139,"file_url":140,"file_type":141,"file_size":142,"view_count":81,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":143,"language":144,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":145,"faqs":146,"seo_title":147,"seo_description":67,"update_tm":148,"read_time":149},137451211410,"https://ap-avatar.wpscdn.com/avatar/2000bb0a9246f588df?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786362646172706240","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nTwo-Tier heuristic search for ransomware-as-a-service based cyberattack défense analysis using explainable Bayesian deep learning model  \nAli Saeed Almuflih1,2􀀍  \nData security assurance is essential owing to the improving popularity of cloud computing and its extensive usage through several industries, particularly in light of the increasing number of cybersecurity attacks. Ransomware-as-a-service (RaaS) attacks are prominent and widespread, allowing uniform individuals with minimum technology to perform ransomware processes. While RaaS methods have declined the access barriers for cyber threats, generative artificial intelligence (AI) growth might result in new possibilities for offenders. The high prevalence of RaaS-based cyberattacks poses essential challenges to cybersecurity, requiring progressive and understandable defensive mechanisms. Furthermore, deep or machine learning (ML) methods mainly provide a black box, giving no data about how it functions. Understanding the details of a classification model’s decision can be beneficial for understanding the work way to be identified. This study presents a novel Two-Tier Metaheuristic Algorithm for Cyberattack Defense Analysis using Explainable Artificial Intelligence based Bayesian Deep Learning (TTMCDA-XAIBDL) method. The main intention oftheTTMCDAXAIBDL method isto detect and mitigate ransomware cyber threats. Initially, the TTMCDA-XAIBDL method performs data preprocessing using Z-score normalization to ensure standardization and scalability of features. Next, the improved sand cat swarm optimization (ISCSO) technique is used for the feature selection. The Bayesian neural network (BNN) is employed to classify cyberattack defence. Moreover, the BNN’s hyperparameters are fine-tuned using the whale optimization algorithm (WOA) model, optimizing its performance for effective detection of ransomware threats. Finally, the XAI using SHAP is integrated to provide explainability, offering perceptions ofthe model’s decision-making procedure and adopting trust in the system. To demonstrate the effectiveness oftheTTMCDA-XAIBDL technique, a series of simulations are conducted using a ransomware detection dataset to evaluate its classification performance. The performance validation oftheTTMCDA-XAIBDL technique portrayed a superior accuracy value of 99.29% over the recent methods.  \nKeywords Ransomware-as-a-service, Metaheuristic, Bayesian neural network, Explainable artificial intelligence, Cyberthreats  \nRansomware threats have recently captivated the attention of cyber security specialists due to the quick enlargement of their attacks and the enlargement of novel variants that can avoid anti-malware and antivirus software1. Nowadays, it is a highly lucrative business for cybercriminals, posturing attacks that are rising in organizations and causing trillions of dollars in economic losses. Ransomware is malicious software that restricts access or encodes data to computer systems, often challenging payment for dismissing the affected files. They usually perform in dual primary methods: locker ransomware and crypto-ransomware2. Crypto ransomware encodes all files in the targeted device, while locker ransomware renders the device inoperable by locking the whole system rather than only particular files. Ransomware threats have substantially harmful effects on IT systems3. These threats’ impacts include information or data loss due to file encryption, economic costs to  \n1Department of Industrial Engineering, College of Engineering, King Khalid University, P.O. Box 394, Abha 61421, Saudi Arabia. 2Center for Engineering and Technology Innovations, King Khalid University, Abha 61421, Saudi Arabia. 􀀍 email: [asalmuflih@kku.edu.sa](asalmuflih@kku.edu.sa)  \n[www. nature.com/scientificreports/](www. nature.com/scientificreports/)  \nbusinesses for incident handling, other security-related problems, and life loss owin","cbCaieAVuxHatOoq","https://ap.wps.com/l/cbCaieAVuxHatOoq","pdf",8769873,26,"English","# Abstract\n# Introduction\n# Related Work and Background\n# Proposed Method: TTMCDA-XAIBDL\n## Data Preprocessing and Normalization\n## Feature Selection with Improved Sand Cat Swarm Optimization\n## Bayesian Neural Network Classification and Hyperparameter Tuning\n## Explainability with SHAP\n# Experimental Setup and Performance Evaluation","[{\"question\":\"What problem does the study address in ransomware-as-a-service attacks?\",\"answer\":\"It targets the growing threat of RaaS-based cyberattacks and the need for progressive defensive mechanisms that are understandable rather than opaque black-box models.\"},{\"question\":\"How does TTMCDA-XAIBDL build its detection pipeline?\",\"answer\":\"It applies Z-score normalization for preprocessing, uses improved sand cat swarm optimization for feature selection, classifies using a Bayesian neural network, and tunes hyperparameters with whale optimization.\"},{\"question\":\"How is explainability provided in the proposed method?\",\"answer\":\"SHAP is integrated as explainable artificial intelligence to reveal insights into the model’s decision-making process and support trust in the system.\"},{\"question\":\"What performance result is reported for the proposed approach?\",\"answer\":\"Experiments on a ransomware detection dataset show a superior accuracy of 99.29% compared with recent methods.\"}]","Two-Tier heuristic search for ransomware-as-a-service based cyberattack défense analysis using explainable Bayesian deep learning model | PDF",1790733226,66]