Hybridizing Base-Line 2D-CNN Model with Cat Swarm Optimization for Enhanced Advanced Persistent Threat Detection
<|reference_start|>Hybridizing Base-Line 2D-CNN Model with Cat Swarm Optimization for Enhanced Advanced Persistent Threat Detection: In the realm of cyber-security, detecting Advanced Persistent Threats (APTs) remains a formidable challenge due to their stealthy and sophisticated nature. This research paper presents an innovative approach that leverages Convolutional Neural Networks (CNNs) with a 2D baseline model, enhanced by the cutting-edge Cat Swarm Optimization (CSO) algorithm, to significantly improve APT detection accuracy. By seamlessly integrating the 2D-CNN baseline model with CSO, we unlock the potential for unprecedented accuracy and efficiency in APT detection. The results unveil an impressive accuracy score of $98.4\%$, marking a significant enhancement in APT detection across various attack stages, illuminating a path forward in combating these relentless and sophisticated threats.<|reference_end|>
@article{bakhiet2024hybridizing,
    title={Hybridizing Base-Line 2D-CNN Model with Cat Swarm Optimization for
  Enhanced Advanced Persistent Threat Detection},
    author={Ali M. Bakhiet and Salah A. Aly},
    journal={arXiv preprint arXiv:2408.17307},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.17307},
    primaryClass={cs.CR cs.AI cs.LG cs.NI}
}