skip to main content
10.1145/3387168.3387218acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicvispConference Proceedingsconference-collections
research-article

A New Cryptojacking Malware Classifier Model Based on Dendritic Cell Algorithm

Published: 25 May 2020 Publication History

Abstract

A new threat known as "cryptojacking" has entered the picture where cryptojacking malware is the future trend for cyber criminals, who infect victim's device, install cryptojacking malware, and use the stolen resources for crytocurrency mining. Worse comes to worst by 2020, researchers estimate there will be 30 billion of IoT devices in the world. IoT devices are vulnerable to attacks because of their simple configuration, unpatched vulnerability and weak passwords. IoT devices also prone to be poorly monitored because of their nature. There is lack of studies that provide in depth analysis on cryptojacking malware classification using machine learning approach where the current research mostly focused on manual analysis of web-based cryptojacking attacks. 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 machine learning technique that may fit in a low processing capability environment such as IoT and Cyber Physical Systems (CPS). This paper aim to disscuss a new approach based on dendritic cell algorithm in order to provide a lightweight cryptojacking classifier model. The output of this paper 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.

References

[1]
Azuan Ahmad, N.B.I. and Kama, M.N., 2017. CloudIDS: Cloud intrusion detection model inspired by dendritic cell mechanism. International Journal of Communication Networks and Information Security (IJCNIS) Vol, 9, pp. 67--75.
[2]
B. Anderson, D. Quist, J. Neil, C. Storlie, and T. Lane (2012) "Graphbased malware detection using dynamic analysis," Journal in Computer Virology, vol. 7, no. 4, pp. 247--258.
[3]
B. B. Rad, M. Masrom, and S. Ibrahim (2012), "Opcodes histogram for classifying metamorphic portable executables malware," in Proceedings of International Conference on e-Learning and e-Technologies in Education (ICEEE), pp. 209--213, IEEE, Lodz, Poland.
[4]
Beek, C., Castillo, C., Cochin, C. and Dolezal, A. (2018). McAfee Labs Threats Report. [ebook] McAfee Labs. Available at: https://www.mcafee.com/enterprise/en-us/assets/reports/rp-quarterly-threats-sep-2018.pdf [Accessed 9 Apr. 2019].
[5]
B. Kang, S. Y. Yerima, K. McLaughlin, and S. Sezer (2016) "N-opcode analysis for Android malware classification and categorization," in Proceedings of International Conference On Cyber Security And Protection Of Digital Services (Cyber Security), pp. 1--7, IEEE, London, UK.
[6]
B. Wolfe, K. Elish, and D. Yao (2014), "High precision screening for Android malware with dimensionality reduction," in Proceedings of 13th International Conference on Machine Learning and Applications (ICMLA), pp. 21--28, IEEE, Detroit, MI, USA
[7]
D. Arp, M. Spreitzenbarth, M. Hübner, H. Gascon, and K. Rieck, (2014), "DREBIN: effective and explainable detection of android malware in your pocket," in Proceedings of 21st Annual Network and Distributed System Security Symposium (NDSS'14), pp. 1--15, San Diego, CA, USA.
[8]
Eskandari, S., Leoutsarakos, A., Mursch, T. and Clark, J., 2018, April. A first look at browser-based Cryptojacking. In 2018 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW) (pp. 58--66). IEEE.
[9]
G. Canfora, A. De Lorenzo, E. Medvet, F. Mercaldo, and C. A. Visaggio (2015), "Effectiveness of opcode ngrams for detection of multi family Android malware," in Proceedings of 10th International Conference on Availability, Reliability and Security (ARES), pp. 333--340, IEEE, Toulouse, France.
[10]
G. Suarez-Tangil, S. K. Dash, M. Ahmadi, J. Kinder, G. Giacinto, and L. Lorenzo Cavallaro (2017), "DroidSieve: fast and accurate classification of obfuscated android malware," in Proceedings of Seventh ACM on Conference on Data and Application Security and Privacy, pp. 309--320, ACM, Scottsdale, AZ, USA.
[11]
Hang, D.O.N.G., HE, N.Q., Ge, H.U., Qi, L.I. and ZHANG, M., 2014. Malware detection method of android application based on simplification instructions. The Journal of China Universities of Posts and Telecommunications, 21, pp. 94--100. Conference Name:ACM Woodstock conference
[12]
I. Santos, F. Brezo, X. Ugarte-Pedrero, and P. G. Bringas (2013), "Opcode sequences as representation of executables for data-mining-based unknown malware detection," Information Sciences, vol. 231, pp. 64--82.
[13]
I. Santos, F. Brezo, B. Sanz, C. Laorden, and P. G. Bringas (2014), "Using opcode sequences in single-class learning to detect unknown malware," IET information security, vol. 5, no. 4, pp. 220--227
[14]
Liebenberg, D., McFarland, C. and Martinez, M. (2018). The Illicit Cryptocurrency Mining Threat. [ebook] Cyber Threat Alliance. Available at: https://www.cyberthreatalliance.org/wp-content/uploads/2018/09/CTA-Illicit-CryptoMining-Whitepaper.pdf [Accessed 7 Apr. 2019].
[15]
M. Zhao, T. Zhang, F. B. Ge, and Z. J. Yuan (2012), "RobotDroid: a lightweight malware detection framework on smartphones," Journal of Networks, vol. 7, no. 4, pp. 715--722.
[16]
N. Runwal, R. M. Low, and M. Stamp (2012), "Opcode graph similarity and metamorphic detection," Journal in Computer Virology, vol. 8, no. 1-2, pp. 37--52.
[17]
P. O'Kane, S. Sezer, and K. McLaughlin (2014), "N-gram density based malware detection," in Proceedings of World Symposium on Computer Applications & Research (WSCAR), pp. 1--6, IEEE, Sousse, Tunisia
[18]
P. OKane, S. Sezer, K. McLaughlin, and E. G. Im (2015), "Svm training phase reduction using dataset feature filtering for malware detection," IEEE transactions on information forensics and security, vol. 8, no. 3-4, pp. 500--509.
[19]
Sun, H., Wang, X., Buyya, R. and Su, J., 2017. CloudEyes: Cloud based malwaredetection with reversible sketch for resource constrained internet of things (IoT)devices. Software: Practice and Experience, 47(3), pp. 421--441.
[20]
Xie, N., Di, X., Wang, X. and Zhao, J., Andro_MD: Android Malware Detection based on Convolutional Neural Networks. International Journal of Performability Engineering, 14(3), p.547, 2018.
[21]
Ahmad, Azuan, et al. "Danger theory based hybrid intrusion detection systems for cloud computing." International Journal of Computer and Communication Engineering 2.6, p.650, 2013.

Index Terms

  1. A New Cryptojacking Malware Classifier Model Based on Dendritic Cell Algorithm

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    ICVISP 2019: Proceedings of the 3rd International Conference on Vision, Image and Signal Processing
    August 2019
    584 pages
    ISBN:9781450376259
    DOI:10.1145/3387168
    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]

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 25 May 2020

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. Artificial Intelligence
    2. Cryptojacking
    3. Cyber Security
    4. Dendritic Cell Algorithm
    5. Malware

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Funding Sources

    Conference

    ICVISP 2019

    Acceptance Rates

    ICVISP 2019 Paper Acceptance Rate 126 of 277 submissions, 45%;
    Overall Acceptance Rate 186 of 424 submissions, 44%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 196
      Total Downloads
    • Downloads (Last 12 months)20
    • Downloads (Last 6 weeks)3
    Reflects downloads up to 25 Feb 2025

    Other Metrics

    Citations

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media