[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120489-en":3,"doc-seo-120489-105":29,"detail-sidebar-cat-0-en-105":90},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},120489,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Mitigating ransomware attacks through cyber threat intelligence and machine learning","Escalating ransomware threats demand proactive, multi-faceted mitigation strategies for organizations facing increasingly sophisticated attack tactics. A unified approach is presented that combines machine learning (ML) with cyber threat intelligence (CTI) to strengthen defenses against ransomware assaults. The methodology applies three ML models—Random Forest, XGBoost, and AdaBoost—to distinguish malicious from ransomware-related software, achieving a reported 98.55% identification rate. CTI further improves situational awareness by providing actionable intelligence for targeted, proactive protection of critical digital assets.","Mitigating ransomware attacks through cyber threat intelligence and machine learning  \nMamady Kante, Vivek Sharma, Keshav Gupta  \nDepartment of Computer Science and Engineering, Sharda University, Greater Noida, India  \nArticle Info ABSTRACT  \nArticle history:  \nReceived Nov 3, 2023 Revised Dec 5, 2023 Accepted Dec 10, 2023  \nKeywords:  \nCyber threat intelligence  \nMachine learning Malware Ransomware Static analysis  \nCorresponding Author:  \nIn the face of escalating cyber threats, particularly the rampant and sophisticated nature of ransomware attacks, organizations are compelled to adopt a proactive and multi-faceted strategy for mitigation. The fusion of machine learning (ML) algorithms enables the system to dynamically adapt and evolve in response to evolving attack vectors and tactics employed by cybercriminals. This paper presents a comprehensive approach that synergistically integrates ML and cyber threat intelligence (CTI) to fortify defenses against ransomware assaults. The proposed methodology incorporates three distinct machine learning techniques, namely random forest (RF), extreme gradient boosting (XGBoost), and adaptive boosting (AdaBoost) . Empirical evidence derived from the study affirms the efficacy of this approach in effectively discriminating between malicious and ransom software, achieving a notable identification rate of 98.55% . The incorporation ofCTI enhances the strategic posture by providing actionable insights into the threat landscape. The proposed focuses on identifying and neutralizing ransomware, aligning with contemporary cybersecurity imperatives, offering a proactive defense against ransomware attacks, ultimately safeguarding critical assets, and preserving the integrity of digital ecosystems.  \nThis is an open access article under the CC BY-SA license.  \nMamady Kante  \nDepartment of Computer and Engineering, Sharda University 201310 Greater Noida, Uttar Pradesh, India [Email: mhdkante@gmail.com](Email: mhdkante@gmail.com)  \n1. INTRODUCTION  \nThe rate at which technology is evolving, demonstrated by internet, has facilitated the enhancement of human existence in terms of convenience and comfort. Nevertheless, this evolution has engendered a reliance on the internet, positioning it as the nucleus of our daily lives. The imperative for constant connectivity, ubiquitously and instantaneously, has intensified the intricacies of the information system, leading to multiple vulnerabilities. Presently, the escalating number and interconnectivity of computers, coupled with the escalating complexity of systems and their facile extensibility, contribute to the escalating incidence of malware infections daily. A prominent and perilous menace to organizational integrity is ransomware [1], [2] . This insidious form of malicious software effectively takes control of a computer system, encrypts files on the hard drive, or forces the computer to shut down, demanding a ransom in return for restoring normal functionality and obstructing user access to the system. This form of cybercrime has grown exponentially in recent years, targeting businesses, healthcare institutions, government agencies, and individuals [3] . The motivation behind ransomware attacks is often financial gain, and the consequences can be catastrophic, ranging from financial losses to reputational damage. The operation of ransomware is described in Figure 1.  \nFigure 1. Ransomware attacks operation [4]  \nThe tenuous state of healthcare delivery amid the COVID-19 pandemic was intricately linked to a surge in ransomware attacks during 2020 [5] . Consequently, medical institutions experienced severe disruptions in healthcare services, accompanied by enduring ramifications. Throughout 2020, a staggering 550,000 ransomware incidents were recorded daily, yielding cyber attackers an estimated 1.5 trillion dollars. In 2021, a notable escalation occurred, with 66% of monitored organizations falling prey to ransomware assaults, a substantial increase","cbCaistMb7A0DADE","https://ap.wps.com/l/cbCaistMb7A0DADE","pdf",422380,1,"English","en",105,"# Abstract\n# Introduction\n## Background and impact of ransomware\n## Related work on ML-based ransomware mitigation\n## CTI and automated response challenges","[{\"question\":\"What is the core approach combining in the document to mitigate ransomware?\",\"answer\":\"The document proposes an integrated approach that synergizes machine learning (ML) algorithms with cyber threat intelligence (CTI) to improve ransomware defense and awareness.\"},{\"question\":\"Which machine learning techniques are used in the proposed methodology?\",\"answer\":\"It uses three ML techniques: Random Forest (RF), Extreme Gradient Boosting (XGBoost), and AdaBoost (adaptive boosting).\"},{\"question\":\"What performance result is reported for distinguishing ransomware-related software?\",\"answer\":\"The study reports an identification rate of 98.55% for discriminating malicious and ransomware software.\"}]","Mitigating ransomware attacks through cyber threat intelligence and machine learning | 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is the core approach combining in the document to mitigate ransomware?","Question",{"text":74,"@type":75},"The document proposes an integrated approach that synergizes machine learning (ML) algorithms with cyber threat intelligence (CTI) to improve ransomware defense and awareness.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which machine learning techniques are used in the proposed methodology?",{"text":79,"@type":75},"It uses three ML techniques: Random Forest (RF), Extreme Gradient Boosting (XGBoost), and AdaBoost (adaptive boosting).",{"name":81,"@type":72,"acceptedAnswer":82},"What performance result is reported for distinguishing ransomware-related software?",{"text":83,"@type":75},"The study reports an identification rate of 98.55% for discriminating malicious and ransomware 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