Hybridizing Base-Line 2D-CNN Model with Cat Swarm Optimization for Enhanced Advanced Persistent Threat Detection

TL;DR

This paper introduces a hybrid approach combining 2D-CNN and Cat Swarm Optimization to improve the accuracy of detecting sophisticated cyber threats known as APTs, achieving over 98% accuracy.

Contribution

The study presents a novel integration of CNN and CSO algorithms specifically tailored for enhanced APT detection in cybersecurity.

Findings

Achieved 98.4% detection accuracy

Enhanced detection across various attack stages

Demonstrated efficiency improvements

Abstract

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.