[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117958-en":3,"doc-seo-117958-105":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":4,"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},117958,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","A Note on Machine Learning Applied in Ransomware Detection - read online free","Ransomware is malicious software that encrypts victims’ data to cause irreversible loss and to generate financial incentives for attackers and organizations. With ransomware campaigns rising and becoming more creative, defending against these threats consumes significant time and energy. This paper examines how machine learning strengthens malware recognition and improves prediction of attack outcomes and behaviors, supporting more accurate anti-ransomware defenses. It also highlights key 2022 ransomware events and the ransom demands reported.","A note on machine learning applied in ransomware detection  \nManuela Horduna 1 , Simona-Maria L˘az˘arescu 1 , and Emil Simion2  \n1 University ”Alexandru Ioan Cuza” Iasi, Faculty of Computer Science  \n2 Polytechnic University of Bucharest  \nAbstract  \nRansomware is a malware that employs encryption to hold a victim’s data, causing irreparable loss and monetary incentives to individuals or business organizations. The occurrence of ransomware attacks has been increasing significantly and as the attackers are investing more creativity and inventiveness into their threats, the struggle of fighting against ill-themed activities has become more difficult and even time and energy-draining. Therefore, recent researches try to shed some light on combining machine learning with defense mechanisms for detecting this type of malware. Machine learning allows anti-ransomware systems to become more accurate at predicting outcomes or behaviors of the attacks and is vastly used in the advanced research of cybersecurity. In this paper we analyze how machine learning can improve malware recognition in order to stand against critical security issues, giving a brief, yet comprehensive overview of this thriving topic in order to facilitate future research. We also briefly present the most important events of 2022 in terms of ransomware attacks, providing details about the ransoms demanded.  \nKeywords—Ransomware, machine learning, malware, cybersecurity  \n1 Introduction  \nMalicious software, shortly malware, is software designed to corrupt or damage a system. We can classify malware based on payload, the way it propagates, or other execution features, and its targets can be individuals, companies, or institutions. The principal types of malware are worms, trojans, viruses, botnets, adware, and ransomware. The COVID-19 pandemic had an important role in the increase in the cyberattack rate because the attackers use this context to spread ransomware through phishing emails. In the middle of the year 2021, The HHS Cybersecurity Program specified that 48 of the 82 global ransomware attack cases reported up to that time targeted the US Healthcare entity.  \nRansomware is a form of malware which have the objective to prevent access to personal information until a ransom will be paid, most often being required cryptocurrency such as Bitcoin for the ransom in order to make it even more difficult to track the transactions. May 2021 represented an important moment in terms of the importance of paying the ransom because it brought to attention the discussion of whether or not the payment of ransom should be made to the attackers. At that time almost $5 million or the equivalent of 75 Bitcoins were paid to the DarkSide group by Colonial Pipeline to release their affected computer system. Paying the ransom can further encourage cybercriminals to carry out such attacks. For this reason, this payment of the ransom could soon become illegal, because there is a legislative framework in the United States that is taking shape in this  \nregard. The capability to analyze a large amount of data fast makes machine learning algorithms an adequate and useful mechanism in ransomware, and more generally malware, detection.  \nNowadays, ransomware is more sophisticated with every attack that appears, but it has its origins in 1989 when one of the first ransomware attacks, called the AIDS trojan, was documented. AIDS Trojan used symmetric cryptography, it was released through a floppy disk and the victims had to pay $189 in order to regain access to their systems.  \n2 Retrospective of ransomware attacks in 2022  \nThe year 2022 brings to light the fact that paradoxically, although the number of ransomware attacks remains a significant one, the affected victims paid approximately 16 million dollars compared to 74 million dollars in 2021, which represents a percentage of only 22% . It is not yet possible to say exactly what generated these things, but it is suspected that a certain ro","cbCaipgt2xKthFru","https://ap.wps.com/l/cbCaipgt2xKthFru","pdf",268290,1,17,"English","en",105,"# Introduction\n## Malware background and ransomware evolution\n## Rationale for machine learning in detection\n# Retrospective of ransomware attacks in 2022\n## January 2022 events\n## February 2022 events","[{\"question\":\"What makes ransomware particularly damaging for victims and organizations?\",\"answer\":\"Ransomware uses encryption to lock access to victims’ data, which can lead to irreparable loss and significant monetary pressure to pay. The attacks are designed to force payment and disrupt operations.\"},{\"question\":\"How does machine learning improve ransomware detection in the proposed research context?\",\"answer\":\"Machine learning helps anti-ransomware systems become more accurate at predicting outcomes and behaviors of attacks. The paper links fast analysis of large datasets with effective detection.\"},{\"question\":\"Which major 2022 ransomware developments are summarized in the document?\",\"answer\":\"The document reviews key events across 2022, including notable January incidents affecting government, healthcare-related systems, and corporate targets, as well as ongoing attacks involving groups such as Conti and BlackCat during February.\"}]","A Note on Machine Learning Applied in Ransomware Detection - read online free | PDF",1785680535,43,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-note-on-machine-learning-applied-in-ransomware-detection-read-online-free","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-note-on-machine-learning-applied-in-ransomware-detection-read-online-free/117958/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What makes ransomware particularly damaging for victims and organizations?","Question",{"text":75,"@type":76},"Ransomware uses encryption to lock access to victims’ data, which can lead to irreparable loss and significant monetary pressure to pay. The attacks are designed to force payment and disrupt operations.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does machine learning improve ransomware detection in the proposed research context?",{"text":80,"@type":76},"Machine learning helps anti-ransomware systems become more accurate at predicting outcomes and behaviors of attacks. 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