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

arXiv: https://arxiv.org/abs/2408.17307 · HTML: https://mlsanity.com/paper/2408.17307/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 29 references that are in the map.
- First posted on arXiv 30 Aug 2024.

## Builds on (most cited first, 2 of 2)

- [Cat Swarm Optimization Algorithm -- A Survey and Performance Evaluation](https://mlsanity.com/paper/2001.11822/) (2020)
- [Malware triage for early identification of Advanced Persistent Threat activities](https://mlsanity.com/paper/1810.07321/) (2018)

## Abstract (arXiv)

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.