Article
Version 1
This version is not peer-reviewed
Malware Detection Using Deep Learning Approaches
Version 1
: Received: 12 July 2024 / Approved: 15 July 2024 / Online: 15 July 2024 (19:46:18 CEST)
How to cite: Shah, A.; Nawaf, L. Malware Detection Using Deep Learning Approaches. Preprints 2024, 2024071214. https://doi.org/10.20944/preprints202407.1214.v1 Shah, A.; Nawaf, L. Malware Detection Using Deep Learning Approaches. Preprints 2024, 2024071214. https://doi.org/10.20944/preprints202407.1214.v1
Abstract
Malware detection is a crucial pillar of cybersecurity and aims to identify and neutralize malware threats. This study investigates how three deep learning algorithms - Convolutional Neural Net-works with One-Dimensional Architecture (CNN1D), Neural Networks (NN), and a hybrid model of one dimensional convolutional neural networks (CNN1D) and Long Short-Term Memory (LSTM) - help detect malware in Portable Executable working (.exe) files. Using a large dataset of tagged malware and benign samples, the effectiveness of these algorithms in distinguishing dangerous from harmless executables is extensively tested. In addition, the study evaluates the computational efficiency of the proposed methods for a variety of hardware configurations, including Central Processing Units (CPU), Graphics Processing Units (GPU), Raspberry Pi (IoT device), and Tensor Processing Units (TPU). Performance metrics provide insight into the scalability and effectiveness of the model across different hardware configurations. The results of this study have significant impact on the development of malware detection, particularly in the context of IoT security. By demonstrating the capabilities and limitations of CNN1D, Neural Networks, and CNN1D with LSTM models, the study provides practitioners with efficient and scalable solutions to real-world malware detection difficulties. In addition, insights from performance tests on various hardware platforms provide important assistance in developing robust security measures in IoT contexts, strengthening cybersecurity defenses in an ever-changing digital landscape.
Keywords
Malware Detection; IoT; Deep Learning; Neural Networks; Long Short-term Memory; Hybrid Model; Central Processing Unit; Graphics Processing Unit; Tensor Processing Unit; Portable Executable
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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