Cryptojacking Malware Detection using the Bayesian Consensus Clustering with Large Iterative Multi-Tier Ensemble in the Cryptocurrency in the Cloud
S. Balamurugan1, M. Thangaraj2
1S. Balamurugan, Research Scholar, Department of Computer Science, Bharathiar University, India.
2M. Thangaraj, Associate Professor, Department of Computer Science, Madurai Kamaraj University, India.
Manuscript received on 01 August 2019. | Revised Manuscript received on 05 August 2019. | Manuscript published on 30 September 2019. | PP: 4256-4264 | Volume-8 Issue-3 September 2019 | Retrieval Number: C5159098319/2019©BEIESP | DOI: 10.35940/ijrte.C5159.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Virtual Currencies and cryptocurrency are a trending digital currency method which uses the Blockchain technology. Cryptocurrency is a digital method designed to exchange the asset between the users based on a powerful cryptography which ensures the transaction are safe and controllable. We have various legal areas identified while using the cryptocurrency, as being the virtual currency, the amount of assets used by the users increases rapidly. With the increase in the asset the security breaches are one of the key vulnerable areas to focus. Cryptocurrency mining malware or Cryptojacking remains a trending terminology which identifies the malicious software or malware developed to use the data from the smart phones and computers. The major threat of the Cryptojacking is cryptocurrency mining without user’s approval. This article implemented based on our CCEC Framework method published for Malware detection in SMS’s for the Smartphone users. The article explains about how the Malware detected using the CCEC Framework. Malwares created in various format so identifying the Malware takes time before which user assets remains vulnerable. So, the proposed method ensures we have a reduction in time by using various online data sources to identify the Cryptojacking malware.
Keywords: About Four Key Words or Phrases in Alphabetical Order, Separated by Commas.
Scope of the Article: Clustering