Ransomware Detection Using Machine Learning in the Linux Kernel
<|reference_start|>Ransomware Detection Using Machine Learning in the Linux Kernel: Linux-based cloud environments have become lucrative targets for ransomware attacks, employing various encryption schemes at unprecedented speeds. Addressing the urgency for real-time ransomware protection, we propose leveraging the extended Berkeley Packet Filter (eBPF) to collect system call information regarding active processes and infer about the data directly at the kernel level. In this study, we implement two Machine Learning (ML) models in eBPF - a decision tree and a multilayer perceptron. Benchmarking latency and accuracy against their user space counterparts, our findings underscore the efficacy of this approach.<|reference_end|>
@article{brodzik2024ransomware,
    title={Ransomware Detection Using Machine Learning in the Linux Kernel},
    author={Adrian Brodzik, Tomasz Malec-Kruszy'nski, Wojciech Niewolski,
  Miko{l}aj Tkaczyk, Krzysztof Bocianiak, Sok-Yen Loui},
    journal={arXiv preprint arXiv:2409.06452},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.06452},
    primaryClass={cs.CR cs.LG}
}