[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123124-en":3,"doc-seo-123124-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123124,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Proactive ransomware prevention in pervasive IoMT via hybrid machine learning","Advancements in information and communications technology (ICT) have reshaped computing through the internet of things (IoT) and its healthcare branch, the internet of medical things (IoMT), while simultaneously introducing serious security risks such as ransomware. To mitigate this threat, the study proposes a scalable hybrid machine learning framework for accurate IoMT ransomware identification. Experiments with a state-of-the-art dataset show improved detection performance, achieving 87% accuracy, and combining multi-stage feature extraction with neural network analysis to distinguish benign and malicious behaviors and support effective threat termination.","Indonesian Journal of Electrical Engineering and Computer Science  \nVol. 34, No. 2, May 2024, pp. 970~982  \nISSN: 2502-4752, DOI: 10. 11591/ijeecs.v34 . i2 .pp970-982 􀂈 970  \n\n| Proactive ransomware prevention in pervasive IoMT via hybrid\u003Cbr>machine learning\u003Cbr>Usman Tariq1, Bilal Tariq2\u003Cbr>1Department of Management Information System, College of Business Administration, Prince Sattam Bin Abdulaziz University,\u003Cbr>Al-Kharj, Saudi Arabia\u003Cbr>2Faculty of Business Administration, COMSATS University Islamabad (CUI), Vehari Campus, Vehari, Pakistan |  |\n| --- | --- |\n| Article Info\u003Cbr>Article history:\u003Cbr>Received Nov 30, 2023 Revised Jan 21, 2024 Accepted Feb 16, 2024\u003Cbr>Keywords:\u003Cbr>Association rules IoMT\u003Cbr>Learning systems Machine learning Ransomware\u003Cbr>Corresponding Author: | ABSTRACT\u003Cbr>Advancements in information and communications technology (ICT) have fundamentally transformed computing, notably through the internet of things (IoT) and its healthcare-focused branch, the internet of medical things (IoMT) . These technologies, while enhancing daily life, face significant security risks, including ransomware. To counter this, the authors present a scalable, hybrid machine learning framework that effectively identifies IoMT ransomware attacks, conserving the limited resources of IoMT devices. To assess the effectiveness of their proposed solution, the authors undertook an experiment using a state-of-the-art dataset. Their framework demonstrated superiority over conventional detection methods, achieving an impressive 87% accuracy rate. Building on this foundation, the framework integrates a multi-faceted feature extraction process that discerns between benign and malign actions, with a subsequent in-depth analysis via a neural network. This advanced analysis is pivotal in precisely detecting and terminating ransomware threats, offering a robust solution to secure the IoMT ecosystem.\u003Cbr>This is an open access article under the CC BY-SA license.\u003Cbr> |\n| Usman Tariq\u003Cbr>Department of Management Information System, College of Business Administration Prince Sattam Bin Abdulaziz University\u003Cbr>Al-Kharj 16278, Saudi Arabia\u003Cbr>[Email: u.tariq@psau.edu.sa](Email: u.tariq@psau.edu.sa) |  |\n\n1. INTRODUCTION  \nThe IoMT is revolutionizing healthcare by facilitating remote monitoring, personalized treatments, and real-time data analytics, improving management of chronic conditions through devices that offer doctors immediate insights into patients ’ health metrics. Studies have demonstrated IoMT ’s efficacy in enhancing patient outcomes, such as improved glycemic control in diabetes [1] and reduced blood pressure in hypertension patients [2], highlighting the role of machine learning in analyzing health data to uncover patterns beneficial for disease management [3] . However, the widespread adoption of IoMT raises significant security concerns, with the sensitivity of health data and the vulnerability of devices to cyber-attacks posing risks to patient safety. Enhancing security in e-health systems is crucial for patient trust and the efficient operation of healthcare services, necessitating advanced risk assessment and protective measures to mitigate potential security breaches. The integration of machine learning for behavior analysis and anomaly detection in IoMT devices presents a promising approach to safeguarding against cyber threats, ensuring the reliability and security of connected healthcare solutions.  \nIn brief, this paper puts forth several noteworthy contributions:  \n􀀐 It introduces a dynamic analysis system that harnesses a hybrid XGBoost and ElasticNet machine learning-based approach for detecting targeted ransomware in the context of the IoMT. What sets this framework apart from existing systems is its utilization of a state-oriented input generation strategy, which enhances code coverage and ultimately leads to an improved overall system performance.  \n􀀐 The approach outlined in this paper was subjected to a rigorous training r","cbCainOGZyK8VtkI","https://ap.wps.com/l/cbCainOGZyK8VtkI","pdf",619698,1,13,"English","en",105,"# Abstract\n# Introduction\n## Contributions\n# Literature Review\n## Machine learning in healthcare","[{\"question\":\"Why is ransomware a concern in IoMT systems?\",\"answer\":\"IoMT adoption increases exposure because health data is highly sensitive and medical devices are vulnerable to cyber-attacks, which can threaten patient safety.\"},{\"question\":\"What approach does the proposed framework use to detect IoMT ransomware?\",\"answer\":\"The framework applies a hybrid machine learning strategy based on XGBoost and ElasticNet, combined with multi-faceted feature extraction and neural-network-based analysis.\"},{\"question\":\"How effective is the proposed method compared with conventional detection techniques?\",\"answer\":\"Experiments using a state-of-the-art dataset indicate the framework outperforms conventional detection methods, reaching about 87% accuracy.\"}]","Proactive ransomware prevention in pervasive IoMT via hybrid machine learning | 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is ransomware a concern in IoMT systems?","Question",{"text":75,"@type":76},"IoMT adoption increases exposure because health data is highly sensitive and medical devices are vulnerable to cyber-attacks, which can threaten patient safety.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What approach does the proposed framework use to detect IoMT ransomware?",{"text":80,"@type":76},"The framework applies a hybrid machine learning strategy based on XGBoost and ElasticNet, combined with multi-faceted feature extraction and neural-network-based analysis.",{"name":82,"@type":73,"acceptedAnswer":83},"How effective is the proposed method compared with conventional detection techniques?",{"text":84,"@type":76},"Experiments using a state-of-the-art dataset indicate the framework outperforms conventional detection methods, reaching about 87% 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