A survey of recent advances in deep learning models for detecting malware in desktop and mobile platforms

P Maniriho, AN Mahmood, MJM Chowdhury - ACM Computing Surveys, 2024 - dl.acm.org
Malware is one of the most common and severe cyber threats today. Malware infects
millions of devices and can perform several malicious activities including compromising …

[HTML][HTML] Recent Applications of Explainable AI (XAI): A Systematic Literature Review

M Saarela, V Podgorelec - Applied Sciences, 2024 - mdpi.com
This systematic literature review employs the Preferred Reporting Items for Systematic
Reviews and Meta-Analyses (PRISMA) methodology to investigate recent applications of …

[HTML][HTML] MalHyStack: a hybrid stacked ensemble learning framework with feature engineering schemes for obfuscated malware analysis

KS Roy, T Ahmed, PB Udas, ME Karim… - Intelligent Systems with …, 2023 - Elsevier
Since the advent of malware, it has reached a toll in this world that exchanges billions of
data daily. Millions of people are victims of it, and the numbers are not decreasing as the …

MDGraph: A novel malware detection method based on memory dump and graph neural network

Q Li, B Zhang, D Tian, X Jia, C Hu - Expert Systems with Applications, 2024 - Elsevier
Malware detection is of great importance to computer security. Although the malware
detection approaches have made great progress in recent years, these methods are still …

[HTML][HTML] Android Malware Detection and Identification Frameworks by Leveraging the Machine and Deep Learning Techniques: A Comprehensive Review

SK Smmarwar, GP Gupta, S Kumar - Telematics and Informatics Reports, 2024 - Elsevier
The ever-increasing growth of online services and smart connectivity of devices have posed
the threat of malware to computer system, android-based smart phones, Internet of Things …

MalDetect: A classifier fusion approach for detection of android malware

M Dhalaria, E Gandotra - Expert Systems with Applications, 2024 - Elsevier
Android has been a significant target of malware applications due to the exponential growth
of mobile devices. This may result in severe threats to Android users such as financial loss …

Effectiveness of machine learning based android malware detectors against adversarial attacks

A Jyothish, A Mathew, P Vinod - Cluster Computing, 2024 - Springer
Android is the most targeted mobile operating system for malware attacks. Most modern anti-
malware solutions largely incorporate deep learning or machine learning techniques to …

[HTML][HTML] MeMalDet: A memory analysis-based malware detection framework using deep autoencoders and stacked ensemble under temporal evaluations

P Maniriho, AN Mahmood, MJM Chowdhury - Computers & Security, 2024 - Elsevier
Malware attacks continue to evolve, making detection challenging for traditional static and
dynamic analysis techniques. On the other hand, memory analysis provides valuable …

Attribution classification method of APT malware based on multi-feature fusion

J Zhang, S Liu, Z Liu - Plos one, 2024 - journals.plos.org
In recent years, with the development of the Internet, the attribution classification of APT
malware remains an important issue in society. Existing methods have yet to consider the …

Malware Detection and Classification System Based on CNN-BiLSTM

H Kim, M Kim - Electronics, 2024 - mdpi.com
For malicious purposes, attackers hide malware in the software used by their victims. New
malware is continuously being shared on the Internet, which differs both in terms of the type …