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A Methodological Approach to Weaponizing Machine Learning

Published: 17 October 2019 Publication History

Abstract

The current literature is replete with studies involving the use of machine learning algorithms for defensive security implementations. For example, machine learning has been utilized to enhance antivirus software and intrusion detection systems. The use of machine learning in defensive cybersecurity operations is well documented. However, there is a substantial gap in the literature on the offensive use of machine learning. Particularly, use of machine learning algorithms to enhance cyber warfare operations. Cyber components to modern conflicts, whether those conflicts are cyber or kinetic warfare, are a fact of the modern international political landscape. It is a natural progression to explore applications of machine learning to cyber warfare, particularly weaponized malware.

References

[1]
S. Cobb & A. Lee. "Malware is called malicious for a reason: The risks of weaponizing code". IEEE 6th International Conference onCyber Conflict (CyCon), 2015.
[2]
M. Connell & S. Vogler. "Russia's Approach to Cyber Warfare." Center for Naval Analyses Arlington United States, 2017.
[3]
Khan, Rafiullah, et al. "Threat Analysis of BlackEnergy Malware for Synchrophasor based Real-time Control and Monitoring in Smart Grid." ICS-CSR. 2016.
[4]
Gronberg. "Rules of Engagement for Cyber Warfare Don't Exist." Government Technology., 2016 http://www.govtech.com/security/Rules-of-Engagement-for-Cyber-Warfare-Dont-Exist.html.
[5]
Wasilow, S. and Thorpe, J.B., 2019. Artificial Intelligence, Robotics, Ethics, and the Military: A Canadian Perspective. AI Magazine, 40(1).
[6]
Vallejos, E.P., Wortham, R. and Miakinkov, E., 2017, June. When AI goes to war: youth opinion, fictional reality and autonomous weapons. In CEPE/ETHICOMP 2017.
[7]
Wang, Y., Friyia, D., Liu, K. and Cohen, R., 2018, March. An Architecture for a Military AI System with Ethical Rules. In 2018 AAAI Spring Symposium Series.
[8]
C. Easttom. "The Role of Weaponized Malware in Cyber Conflict and Espionage." ICCWS 2018 13th International Conference on Cyber Warfare and Security. Academic Conferences and publishing limited, 2018.
[9]
K. Zetter. Countdown to Zero Day: Stuxnet and the launch of the world's first digital weapon. Broadway books, 2014.
[10]
P. Singer. "Stuxnet and its hidden lessons on the ethics of cyberweapons." Case W. Res. J. Int'l L. 47, 2015.
[11]
C. Easttom "An Examination of the Operational Requirements of Weaponized Malware". Journal of Information Warfare 17 (2), 2018.
[12]
R. Khan, et al. "Threat Analysis of BlackEnergy Malware for Synchrophasor based Real-time Control and Monitoring in Smart Grid." ICS-CSR. 2016.
[13]
T. Pultarova. "News Briefing: Cyber security-Ukraine grid hack is wake-up call for network operators". Engineering & Technology, 11(1), 12--13, 2016.
[14]
C. Bronk & E. Tikk-Ringas "The cyber-attack on Saudi Aramco." Survival, 55(2), 81--96. 2013.
[15]
Z. Dehlawi & N. Abokhodair "Saudi Arabia's response to cyber conflict: A case study of the Shamoon malware incident." In Intelligence and Security Informatics (ISI), 2013 IEEE International Conference on (pp. 73--75). IEEE.
[16]
M. Shepperd, D. Bowes, & T. Hall. "Researcher bias: The use of machine learning in software defect prediction." IEEE Transactions on Software Engineering 40.6 (2014): 603--616.
[17]
R. Deo & S. Mehmet. "Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia." Atmospheric Research 153 (2015): 512--525.
[18]
S. Dua & D. Xian. Data mining and machine learning in cybersecurity. Auerbach Publications, 2016.
[19]
A. Buczak & G. Erhan. "A survey of data mining and machine learning methods for cyber security intrusion detection." IEEE Communications Surveys & Tutorials 18.2 (2016): 1153--1176.
[20]
D. Sahoo, L. Chenghao, & C. Steven. "Malicious URL detection using machine learning: A survey." arXiv preprint arXiv:1701.07179, 2017.
[21]
L. Amini, et al. "Adaptive cyber-security analytics." U.S. Patent No. 9,032,521. 12 May 2015.
[22]
E. Nakashima."Obama moves to split cyberwarfare command from the NSA." The Washington Post (2016).
[23]
M. Robinson, K. Kevin, & H. Janicke. "Cyber warfare: Issues and challenges." Computers & security 49 (2015): 70--94.
[24]
A. Colarik & L. Janczewski. "Establishing cyber warfare doctrine." Current and Emerging Trends in Cyber Operations. Palgrave Macmillan, London, 2015. 37--50.
[25]
M. Mehryar, A. Rostamizadeh, & A. Talwalkar. Foundations of machine learning. MIT press, 2018.
[26]
D. Hand. "Evaluating Statistical and Machine Learning Supervised Classification Methods." Statistical Data Science (2018): 37.
[27]
O. Simeone. "A brief introduction to machine learning for engineers". Foundations and Trends® in Signal Processing, 12(3-4), 200--431, 2018.
[28]
S. Shalev-Shwartz & D. Understanding machine learning: From theory to algorithms. Cambridge university press, 2014.
[29]
G. Tanu. "THE USE OF PATRIOT SURFACE-TO-AIR MISSILE SYSTEMS DURING THE MODERN MILITARY CONFLICTS." International Scientific Conference" Strategies XXI". Vol. 2. " Carol I" National Defence University, 2018.
[30]
A. Mohaisen & O. Alrawi, "Unveiling zeus: automated classification of malware samples." In Proceedings of the 22nd International Conference on World Wide Web (pp. 829--832). ACM. 2013.
[31]
G. Zhao, K. Xu, L. Xu, L., & B. Wu, " Detecting APT malware infections based on malicious DNS and traffic analysis." IEEE access, 3, 1132--1142. 2015.
[32]
J. Lalande & S. Wendzel " Hiding privacy leaks in android applications using lowattention raising covert channels." In 2013 International Conference on Availability, Reliability and Security (pp. 701--710). IEEE. 2013.
[33]
R. Raveendranath, V. Rajamani, A. Babu, & S. Datta, "Android malware attacks and countermeasures: Current and future directions." In 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT) (pp. 137--143). IEEE. 2014.

Cited By

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  • (2024)The Weaponization of Artificial Intelligence in Cybersecurity: A Systematic ReviewProcedia Computer Science10.1016/j.procs.2024.06.206239(547-555)Online publication date: 2024
  • (2023)An Overview of Artificial Intelligence Used in MalwareNordic Artificial Intelligence Research and Development10.1007/978-3-031-17030-0_4(41-51)Online publication date: 2-Feb-2023

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  1. A Methodological Approach to Weaponizing Machine Learning

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    cover image ACM Other conferences
    AIAM 2019: Proceedings of the 2019 International Conference on Artificial Intelligence and Advanced Manufacturing
    October 2019
    418 pages
    ISBN:9781450372022
    DOI:10.1145/3358331
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    Publication History

    Published: 17 October 2019

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    Author Tags

    1. Weaponized malware
    2. cyber warfare
    3. machine learning
    4. weaponized malware

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    View all
    • (2024)The Weaponization of Artificial Intelligence in Cybersecurity: A Systematic ReviewProcedia Computer Science10.1016/j.procs.2024.06.206239(547-555)Online publication date: 2024
    • (2023)An Overview of Artificial Intelligence Used in MalwareNordic Artificial Intelligence Research and Development10.1007/978-3-031-17030-0_4(41-51)Online publication date: 2-Feb-2023

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