Evasion-Aware Botnet Attack Detection using Deep Reinforcement Adversarial Learning

Authors

  • Ketan Anand Assistant Professor, Dept of CSE(AI&ML), Sreenidhi Institute of Science and Technology, Yamnempet, Ghatkesar, Hyderabad
  • Raja Rao Budaraju Senior Member of Technical staff, Oracle 3990, Scottfield street Dublin 94568, CA, USA
  • Sumit Kumar Associate Professor & Head, Dept of Artificial Intelligence & Machine Learning, COER University, Roorkee
  • B. Madhav Rao Associate Professor, Department of CSE, SIR C R Reddy College of Engineering, Eluru, AP
  • Basant Sah Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India

Keywords:

Deep reinforcement learning, Botnet, EVGAN, ACGAN, DRLEVGAN

Abstract

Adversarial evasions represent contemporary challenges for applications relying on Machine Learning (ML). The susceptibility of traditional ML inference systems introduces vulnerabilities that make botnet detectors susceptible to attacks through adversarial examples. Complex AI models and sophisticated attack techniques can be used to generate the evasions. One of the potential sources of evasion assaults is generative AI models. The lack of data, which causes ML classifiers to train with a bias toward samples from the majority of classes, is a serious concern as well. This paper proposed a novel “Deep Reinforcement Learning based Evasion Generative Adversarial Network” (DRLEVGAN) to protect evasion attacks and retain semantics of the attack sample. The proposed model also tackles the issues of data imbalance, evasion awareness, and maintaining functionality in the context of synthetic botnet traffic generation. This model does not need adversarial training for the machine learning classifiers since it can act as an adversarial-aware botnet detection model. DRLEVGAN demonstrates superior performance when compared to similar models such as “Auxiliary Classifier GAN (ACGAN) and Evasion Generative Adversarial Network (EVGAN)”.

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Published

24.11.2023

How to Cite

Anand, K. ., Budaraju, R. R. ., Kumar, S. ., Rao, B. M. ., & Sah, B. . (2023). Evasion-Aware Botnet Attack Detection using Deep Reinforcement Adversarial Learning. International Journal of Intelligent Systems and Applications in Engineering, 12(5s), 37–45. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/3819

Issue

Vol. 12 No. 5s (2024)

Section

Research Article

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