Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
With the rapid proliferation of cryptocurrencies, malicious actors are increasingly deploying techniques such as cryptojacking to illicitly mine cryptocurrencies using victims' computing resources. Traditional signature-based detection methods are often ineffective against these evolving threats. In this paper, we propose a novel approach for detecting malicious mining code using machine learning algorithms. By leveraging features extracted from system behavior, file characteristics, and network traffic patterns, our model can effectively distinguish between legitimate and malicious mining activities. We evaluate our approach using a diverse dataset of benign and malicious samples and demonstrate its efficacy in accurately identifying malicious mining code with high precision and recall. Our findings underscore the potential of machine learning in bolstering defenses against the growing menace of cryptojacking attacks.
"Malicious mining Code detection Using Machine Learning Algorithms ", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.9, Issue 5, page no.36 - 38, May-2024, Available :http://www.ijrti.org/papers/IJRTI2405006.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator