Intelligent Malware - Trends and Possibilities

  • Jan Plucar Department of Computer Science, Faculty of Electrical Engineering and Computer Science VSB-TU
  • Jiří Frank Department of Computer Science, Faculty of Electrical Engineering and Computer Science VSB-TU
  • Daniel Walter Department of Computer Science, Faculty of Electrical Engineering and Computer Science VSB-TU
  • Ivan Zelinka Department of Computer Science, Faculty of Electrical Engineering and Computer Science VSB-TU
Keywords: malware, artificial intelligence, swarm, artificial neural network

Abstract

In recent months and years, with more and more computers and computer systems becoming the target of cyberattacks. These attacks are gaining strength and the sophistication of the approach in terms of how to attack. Attackers and Defenders are increasingly using artificial intelligence methods to maximize the success of their actions. For a successful defence, we must be able to anticipate future threats that may come. For these reasons, our research group is engaged in creating experimental software with artificial intelligence to test the possibilities and capabilities of such malware in the event of its deployment. This software has not only malware capabilities but also antimalware and can be used on both sides. This article introduces the reader to the main principles of our design, which can serve as a future platform for cyber defence systems.

References

Amer, E., and Zelinka, I. A dynamic windows malware detection and prediction method based on contextual understanding of api call sequence. Computers & Security 92 (2020), 101760.

Anderson, H. S., Kharkar, A., Filar, B., Evans, D., and Roth, P. Learning to evade static pe machine learning malware models via reinforcement learning. arXiv preprint arXiv:1801.08917 (2018).

Bekerman, D., Shapira, B., Rokach, L., and Bar, A. Unknown malware detection using network traffic classification. In 2015 IEEE Conference on Communications and Network Security (CNS) (2015), IEEE, pp. 134-142.

Bose, S., Barao, T., and Liu, X. Explaining ai for malware detection: Analysis of mechanisms of malconv. In 2020 International Joint Conference on Neural Networks (IJCNN) (2020), IEEE, pp. 1-8.

Case, D. U. Analysis of the cyber attack on the ukrainian power grid. Electricity Information Sharing and Analysis Center (E-ISAC) 388 (2016).

Clarke, R. A., and Knake, R. K. Cyber war. Tantor Media, Incorporated Old Saybrook, 2014.

Kolosnjaji, B., Eraisha, G., Webster, G., Zarras, A., and Eckert, C. Empowering convolutional networks for malware classification and analysis. In 2017 International Joint Conference on Neural Networks (IJCNN) (2017), IEEE, pp. 3838-3845.

Kolosnjaji, B., Zarras, A., Webster, G., and Eckert, C. Deep learning for classification of malware system call sequences. In Australasian Joint Conference on Artificial Intelligence (2016), Springer, pp. 137-149.

Leech, M. D. SOCKS Protocol Version 5. RFC 1928, Mar. 1996.

Lysenko, S. Self-adaptive method for the computer systems resilience in the presence of cyberthreads. RADIOELECTRONIC AND COMPUTER SYSTEMS, 4 (2019), 4-16.

Mayer, M. Artificial intelligence and cyber power from a strategic perspective. Forsvarets hogskole, IFS Insights (2018).

Raff, E., Barker, J., Sylvester, J., Brandon, R., Catanzaro, B., and Nicholas, C. Malware detection by eating a whole exe. arXiv preprint arXiv:1710.09435 (2017).

Rossow, C., Dietrich, C. J., Bos, H., Cavallaro, L., Van Steen, M., Freiling, F. C., and Pohlmann, N. Sandnet: Network traffic analysis of malicious software. In Proceedings of the First Workshop on Building Analysis Datasets

and Gathering Experience Returns for Security (2011), pp. 78-88.

Sharikov, P. Artificial intelligence, cyberattack, and nuclear weapons|a dangerous combination. Bulletin of the Atomic Scientists 74, 6 (2018), 368-373.

Thanh, C., and Zelinka, I. A survey on artificial intelligence in malware as next-generation threats. MENDEL 25, 2 (Dec. 2019), 27-34.

Truong, T., Diep, Q., Zelinka, I., and Dao, T. X-swarm: The upcoming swarm worm. MENDEL 26, 1 (Aug. 2020), 7-14.

Zelinka, I., Das, S., Sikora, L., and Senkerik, R. Swarm virus-next-generation virus and antivirus paradigm? Swarm and Evolutionary Computation 43 (2018), 207-224.

Published
2021-06-21
How to Cite
[1]
Plucar, J., Frank, J., Walter, D. and Zelinka, I. 2021. Intelligent Malware - Trends and Possibilities. MENDEL. 27, 1 (Jun. 2021), 18-22. DOI:https://doi.org/10.13164/mendel.2021.1.018.
Section
Research articles