CNN-LSTM Hybrid Model for Enhanced Malware Analysis and Detection

Abstract

The ever-evolving tactics employed by malware authors to avoid detection pose challenges to the conventional static analysis method, which entails examining the malware code. These challenges arise from the authors’ capacity to obfuscate their code. To address this matter and enhance the identification of malware, integrating dynamic detection and machine learning has emerged as a highly promising approach. This methodology has demonstrated efficacy in identifying malware specifically engineered to circumvent established detection techniques. Behavioural analysis is a crucial component in ensuring endpoints’ security, with the CNN-LSTM algorithm being particularly notable for its effectiveness in identifying Zero-Day malware. This type of malware poses a substantial obstacle to conventional signature-based approaches. This paper aims to assess the efficacy of the Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model, emphasising its significance in tackling the continuously evolving realm of cybersecurity obstacles. The research highlights the significance of transitioning from traditional signature-based detection methods to behavioural analysis techniques. It suggests utilising deep learning approaches such as Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) to improve the ability to detect malware in an environment where threats constantly evolve. The malware detection system that has been developed encompasses a log parser analyser, API monitoring, and an extension checker module. The CNN-LSTM model demonstrates a commendable ability to accurately identify malicious behaviour, achieving a validation accuracy of 96%. This study demonstrates the efficacy of employing behavioural analysis and deep learning techniques to enhance cybersecurity, particularly in addressing sophisticated, evasive, and previously unknown malware risks.