Title: Deep Learning for Malware Detection: A Survey.

Authors: Dr. Michael David

Abstract: This survey provides a comprehensive overview of the application of deep learning techniques to the detection of malware, a rapidly evolving and challenging problem in cybersecurity. Deep learning, with its ability to learn complex patterns and features from large datasets, has emerged as a promising approach for detecting various types of malware, including viruses, worms, trojans, and ransomware. The paper discusses the different deep learning architectures, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and autoencoders, that have been employed for malware detection. It also explores the challenges and limitations associated with deep learning for malware detection, such as the need for large datasets and the potential for adversarial attacks. 

Keywords: Malware detection, Convolutional neural networks (CNNs),  Machine learning, Deep learning

International Journal of Applied Pattern Recognition, 2024 Vol.8 No.2, pp.26-37 

Received: 15 May 2024
Accepted: 16 June 2024
Published online: 24 Aug 2024