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Advancing malware imagery classification with explainable deep learning: A state-of-the-art approach using SHAP, LIME and Grad-CAM. [PDF]

PLoS One
Nazim S, Alam MM, Rizvi SS, Mustapha JC, Hussain SS, Suud MM.   +5 more
europepmc   +1 more source

Hybrid deep learning model for accurate and efficient android malware detection using DBN-GRU. [PDF]

PLoS One
Kauser Sk H, Anu V M.
europepmc   +1 more source

Detección de malware en android utilizando aprendizaje automático.

, 2018
Alejandro Pérez Fals
openalex   +1 more source

Some of the next articles are maybe not open access.

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Dynamic Prototype Network Based on Sample Adaptation for Few-Shot Malware Detection

IEEE Transactions on Knowledge and Data Engineering, 2023
The continuous increase and spread of malware have caused immeasurable losses to social enterprises and even the country, especially unknown malware. Most existing methods use predefined class samples to train models, which cannot handle unknown malware ...
Yuhan Chai, Lei Du, Jing Qiu, Lihua Yin, Zhihong Tian   +4 more
semanticscholar   +1 more source

Practical Malware Analysis

Network Security, 2023
Objective. Currently, the main method of attack on organizations is malware. The problem of strengthening protection against this type of attack remains relevant and requires new approaches.
K. Kendall, Charles F. McMillan
semanticscholar   +1 more source

IMCFN: Image-based malware classification using fine-tuned convolutional neural network architecture

Comput. Networks, 2020
The volume, type, and sophistication of malware is increasing. Deep convolutional neural networks (CNNs) have lately proven their effectiveness in malware binary detection through image classification.
Danish Vasan, M. Alazab, Sobia Wassan, Hamad Naeem, Babak Safaei, Zheng Qin   +5 more
semanticscholar   +1 more source

Deep Learning for Zero-day Malware Detection and Classification: A Survey

ACM Computing Surveys, 2023
Zero-day malware is malware that has never been seen before or is so new that no anti-malware software can catch it. This novelty and the lack of existing mitigation strategies make zero-day malware challenging to detect and defend against.
Fatemeh Deldar, M. Abadi
semanticscholar   +1 more source

Joint Differential Game and Double Deep Q-Networks for Suppressing Malware Spread in Industrial Internet of Things

IEEE Transactions on Information Forensics and Security, 2023
Industrial Internet of Things (IIoT), which has the capability of perception, monitoring, communication and decision-making, has already exposed more security problems that are easy to be invaded by malware because of many simple edge devices that help ...
Shigen Shen, Lanlan Xie, Yanchun Zhang, Guowen Wu, Hong Zhang, Shui Yu   +5 more
semanticscholar   +1 more source

Comprehensive Android Malware Detection Based on Federated Learning Architecture

IEEE Transactions on Information Forensics and Security, 2023
Android malware and its variants are a major challenge for mobile platforms. However, there are two main problems in the existing detection methods: $a$ ) The detection method lacks the evolution ability for Android malware, which leads to the low ...
Wenbo Fang, Junjiang He, Wenshan Li, Xiao Lan, Yang Chen, Tao Li, Jiwu Huang, Linlin Zhang   +7 more
semanticscholar   +1 more source

A Survey of Adversarial Attack and Defense Methods for Malware Classification in Cyber Security

IEEE Communications Surveys and Tutorials, 2023
Malware poses a severe threat to cyber security. Attackers use malware to achieve their malicious purposes, such as unauthorized access, stealing confidential data, blackmailing, etc. Machine learning-based defense methods are applied to classify malware
Senming Yan, Jing Ren, Wei Wang, Limin Sun, W. Zhang, Quan Yu   +5 more
semanticscholar   +1 more source