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Crytojacking Classification based on Machine Learning Algorithm

Published: 25 May 2020 Publication History

Abstract

The rise of cryptocurrency has resulted in a number of concerns. A new threat known as cryptojacking" has entered the picture where cryptojacking malware is the trend for future cyber criminals, who infect computers, install cryptocurrency miners, and use stolen information from victim databases to set up wallets for illicit funds transfers. Worst by 2020, researchers estimate there will be 30 billion of IoT devices in the world. Majority of the devices are highly vulnerable to simple attacks based on weak passwords and unpatched vulnerabilities and poorly monitored. Thus it is the best projection that IoT become a perfect target for cryptojacking malwares. There are lacks of study that provide in depth analysis on cryptojacking malware especially in the classification model. As IoT devices requires small processing capability, a lightweight model are required for the cryptojacking malware detection algorithm to maintain its accuracy without sacrificing the performance of other process. As a solution, we propose a new lightweight cryptojacking classifier model based on instruction simplification and machine learning technique that can detect the cryptojacking classification algorithm. This research aims to study the features of existing cryptojacking classification algorithm, to enhanced existing algorithm and to evaluate the enhanced algorithm for cryptojacking malware classification. The output of this research will be significant used in detecting cryptojacking malware attacks that benefits multiple industries including cyber security contractors, oil and gas, water, power and energy industries which align with the National Cyber Security Policy (NCSP) which address the risks to the Critical National Information Infrastructure (CNII).

References

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Carlin, D., OrKane, P., Sezer, S., & Burgess, J. (2018). Detecting Cryptomining Using Dynamic Analysis. In 2018 16th Annual Conference on Privacy, Security and Trust (PST) (pp. 1--6). IEEE. https://doi.org/10.1109/PST.2018.8514167
[2]
Hong, G., Yang, Z., Yang, S., Zhang, L., Nan, Y., Zhang, Z., ... Duan, H. (2018). How You Get Shot in the Back: A Systematical Study about Cryptojacking in the Real World. CCS '18 (ACM Conference on Computer and Communications Security), 13. https://doi.org/10.1145/3243734.3243840
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Ahmed, M. E., Nepal, S., & Kim, H. (2018). MEDUSA: Malware Detection Using Statistical Analysis of System's Behavior. In 2018 IEEE 4th International Conference on Collaboration and Internet Computing (CIC) (pp. 272--278). IEEE.
[4]
Wang, W., Ferrell, B., Xu, X., Hamlen, K. W., & Hao, S. (2018). SEISMIC: SEcure in-lined script monitors for interrupting cryptojacks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11099 LNCS, pp. 122--142).
[5]
Ning, R., Wang, C., Xin, C., Li, J., Zhu, L., & Wu, H. (2018). CapJack: Capture In-Browser Crypto-jacking by Deep Capsule Network through Behavioral Analysis, (April).
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Liu, J., Zhao, Z., Cui, X., Wang, Z., & Liu, Q. (2018). A novel approach for detecting browser-based silent miner. Proceedings -2018 IEEE 3rd International Conference on Data Science in Cyberspace, DSC 2018, 490--497.

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  • (2021)Synergy of Blockchain Technology and Data Mining Techniques for Anomaly DetectionApplied Sciences10.3390/app1117798711:17(7987)Online publication date: 29-Aug-2021

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  1. Crytojacking Classification based on Machine Learning Algorithm

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    ICCBN '20: Proceedings of the 2020 8th International Conference on Communications and Broadband Networking
    April 2020
    95 pages
    ISBN:9781450375047
    DOI:10.1145/3390525
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    • Auckland University of Technology

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 25 May 2020

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    Author Tags

    1. Classification
    2. Cryptojacking
    3. Cryptomining
    4. Machine learning
    5. Malicious software

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    Funding Sources

    • Universiti Sains Islam Malaysia Grant

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    ICCBN '20

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    • (2021)Synergy of Blockchain Technology and Data Mining Techniques for Anomaly DetectionApplied Sciences10.3390/app1117798711:17(7987)Online publication date: 29-Aug-2021

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