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It examines ransomware, crypto jacking, and supply chain attacks, then evaluates ML and deep learning for detection and prevention. The analysis compares traditional approaches with deep learning effectiveness to support stronger defenses.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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Impact, and Detection of Cryptocurrency-Related  \nThreats  \nDhanraj Dhotre1, Pankaj R Chandre2, Anand Khandare3, Megharani Patil4, Gopal S Gawande5  \n1Associate Professor, Department of Computer Science and Engineering, MIT School of Computing, MIT Art Design and Technology  \nUniversity, Loni, Pune, India  \n2Associate Professor, Department of Computer Science and Engineering, MIT School of Computing, MIT Art Design and Technology  \nUniversity, Loni, Pune, India  \n3Associate Professor, Department of Computer Engineering, Thakur College of Engineering and Technology, Mumbai  \n4Associate Professor, Department of Computer Engineering, Thakur College of Engineering and Technology, Mumbai  \n5Associate professor, Department of E & TC Engineering, Marathwada Mitra Mandlas College of Engineering, Karve Nagar, Pune  \n1,2,3,4,[5](5dhanraj.dhotre@mituniversity.edu.in)[dhanraj.dhotre@mituniversity.edu.in](5dhanraj.dhotre@mituniversity.edu.in),[pankaj.chandre@mituniversity.edu.in](pankaj.chandre@mituniversity.edu.in), [anand.khandare@thakureducation.org](anand.khandare@thakureducation.org), [megharani.patil@thakureducation.org](megharani.patil@thakureducation.org), [gopalgawande@mmcoe.edu.in](gopalgawande@mmcoe.edu.in)  \nAbstract: Crypto malware has become a major threat to the security of cryptocurrency holders and exchanges. As the popularity of cryptocurrency continues to rise, so too does the number and sophistication of crypto malware attacks. This paper leverages machine learning techniques to understand the evolution, impact, and detection of cryptocurrency-related threats. We analyse the different types of crypto malware, including ransomware, crypto jacking, and supply chain attacks, and explore the use of machine learning algorithms for detecting and preventing these threats. Our research highlights the importance of using machine learning for detecting crypto malware and compares the effectiveness of traditional methods with deep learning techniques. Through this analysis, we aim to provide insights into the growing threat of crypto malware and the potential benefits of using machine learning in combating these attacks.  \nKeywords: Crypto malware, cryptocurrency, machine learning, deep learning, ransomware, crypto jacking, supply chain attacks, cybersecurity.  \nI. Introduction:  \nThe advent of cryptocurrencies fundamentally altered the way we see money and financial activities. The crypto virus is just one of the many dangers this new technology poses[1] . Malicious software referred to as \"crypto malware\" targets cryptocurrency exchanges and users in order to mine or steal bitcoin without the user's knowledge or agreement. As bitcoin malware grows in sophistication and difficulty to detect, it has become a serious issue for the cryptocurrency ecosystem[2][3] . Researchers have started looking into the use of machine learning approaches for identifying and preventing crypto virus to counter these dangers. Machine learning algorithms are the best choice for recognising crypto virus attacks because they have the capacity to find patterns and abnormalities in massive datasets[4] . This article intends to investigate the evolution, consequences, and detection of risks related to cryptocurrencies, as well as the possible advantages of applying machine learning to thwart these attacks[5][6] . We'll start by discussing the various types of cryptocurrency malware and how they impact the community, including supply chain  \nattacks, ransomware, and cryptojacking. We'll then discuss how to identify and steer clear of crypto viruses using machine learning and deep learning methods[7][8] . Following that, the efficacy of machine learning strategies will be contrasted with that of traditional strategies, with a focus on the potential benefits of using machine learning in the fight against crypto virus[9] . The main goal of this research is to give r","cbCaiuvRDdAcoL8s","https://ap.wps.com/l/cbCaiuvRDdAcoL8s","pdf",242343,"English","# Introduction\n# Literature Survey","[{\"question\":\"What problem does the paper address about cryptocurrency security?\",\"answer\":\"The paper addresses how crypto malware targets cryptocurrency users and exchanges to mine or steal assets without consent, and how these attacks are increasing in sophistication and detection difficulty.\"},{\"question\":\"Which types of crypto malware does the paper discuss?\",\"answer\":\"The paper discusses ransomware, crypto jacking (cryptojacking), and supply chain attacks, focusing on their evolution, impact, and risk characteristics.\"},{\"question\":\"How does the paper use machine learning to support detection and defense?\",\"answer\":\"It uses machine learning algorithms to identify patterns and abnormalities in large datasets, and compares ML/deep learning performance with traditional strategies for detection and prevention.\"}]","The Rise of Crypto Malware - Leveraging Machine Learning Techniques to Understand the Evolution, Impact, and Detection of Cryptocurrency-Related Threats | PDF"]