Document Type
Article
Source of Publication
Security and Communication Networks
Publication Date
10-10-2022
Abstract
Android and Windows are the predominant operating systems used in mobile environment and personal computers and it is expected that their use will rise during the next decade. Malware is one of the main threats faced by these platforms as well as Internet of Things (IoT) environment and the web. With time, these threats are becoming more and more sophisticated and detecting them using traditional machine learning techniques is a hard task. Several research studies have shown that deep learning methods achieve better accuracy comparatively and can learn to efficiently detect and classify new malware samples. In this paper, we present a systematic literature review of the recent studies that focused on intrusion and malware detection and their classification in various environments using deep learning techniques. We searched five well-known digital libraries and collected a total of 107 papers that were published in scholarly journals or preprints. We carefully read the selected literature and critically analyze it to find out which types of threats and what platform the researchers are targeting and how accurately the deep learning-based systems can detect new security threats. This survey will have a positive impact on the learning capabilities of beginners who are interested in starting their research in the area of malware detection using deep learning methods. From the detailed critical analysis, it is identified that CNN, LSTM, DBN, and autoencoders are the most frequently used deep learning methods that have effectively been used in various application scenarios.
DOI Link
ISSN
Publisher
Hindawi Limited
Volume
2022
First Page
1
Last Page
31
Disciplines
Computer Sciences
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Ali, Rahman; Ali, Asmat; Iqbal, Farkhund; Hussain, Mohammed; and Ullah, Farhan, "Deep Learning Methods for Malware and Intrusion Detection: A Systematic Literature Review" (2022). All Works. 5406.
https://zuscholars.zu.ac.ae/works/5406
Indexed in Scopus
no
Open Access
yes
Open Access Type
Gold: This publication is openly available in an open access journal/series