Enhancing Cryptojacking Detection Through Hybrid Black Widow Optimization and Generative Adversarial Networks

Cybercriminals now find cryptocurrency mining to be a lucrative endeavour. This is frequently seen in the form of cryptojacking, which is the illegal use of computer resources for cryptocurrency mining. Protecting user resources and preserving the integrity of digital ecosystems depend heavily on the detection and mitigation of such threats. This research presents a unique method that combines Black Widow Optimisation (HBWO) with Generative Adversarial Networks (GANs) to improve the detection of cryptojacking. Due to its covert nature and tendency to elude conventional detection methods, cryptojacking is still a widespread concern. In order to overcome this difficulty, our work makes use of the complementary abilities of deep learning and metaheuristic optimisation. To maximise feature selection for efficient identification of cryptojacking activity, BWO—which draws inspiration from the foraging behaviour of spiders—is utilised. Simultaneously, GANs are employed to produce artificial intelligence (AI) augmentations, which strengthen the detection model's resilience and enrich the training dataset. Utilising HBWO to identify the most discriminative features is the first step in our technique, which also includes preprocessing the dataset to extract pertinent features. The training dataset is then supplemented with artificial data samples created using GANs, which enhances the detection model's capacity for generalisation. Experiments conducted on real-world datasets show the effectiveness of our solution, outperforming baseline techniques. The hybrid technique that has been suggested offers a viable way to improve the detection of cryptojacking. Through the combination of HBWO for feature optimisation and GANs for data augmentation, our approach demonstrates improved 98.02% accuracy and resilience in detecting cryptojacking activity. With its novel framework for fending against new dangers in the digital sphere, this research adds to the continuing efforts in cybersecurity.

@article{Kale2024,
  title     = {Enhancing Cryptojacking Detection Through Hybrid Black Widow Optimization and Generative Adversarial Networks},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {3},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Meenal R. Kale and Deepa and Anil Kumar N and N. Lakshmipathi Anantha and Vuda Sreenivasa Rao and Sanjiv Rao Godla and E. Thenmozhi},
  doi       = {10.14569/IJACSA.2024.0150387},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150387}
}