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.

翻译：在网络安全领域，由于高级持续性威胁（APTs）具有隐蔽性和复杂性，其检测仍是一项艰巨挑战。本研究提出一种创新方法，利用卷积神经网络（CNNs）的二维基线模型，并结合前沿的猫群优化（CSO）算法进行增强，从而显著提升APT检测准确率。通过将2D-CNN基线模型与CSO算法无缝集成，我们实现了APT检测在准确性与效率上的突破性提升。实验结果显示，该方法取得了$98.4\%$的优异准确率，标志着在APT各攻击阶段的检测能力均获得显著增强，为应对这类持续且复杂的威胁指明了前进方向。