Results 81 to 90 of about 2,609 (164)

FedDroidMeter: A Privacy Risk Evaluator for FL-Based Android Malware Classification Systems. [PDF]

Entropy (Basel), 2023
Jiang C, Xia C, Liu Z, Wang T.
europepmc   +1 more source

MalFuzz: Coverage-guided fuzzing on deep learning-based malware classification model. [PDF]

PLoS One, 2022
Liu Y, Yang P, Jia P, He Z, Luo H.
europepmc   +1 more source

TRConv: Multi-Platform Malware Classification via Target Regulated Convolutions

IEEE Access
Malware is an important threat to digital workflow. Traditional malware modeling approaches focused on using hand-crafted features while recent approaches proved the necessity of using learning based methodologies.
Alper Egitmen, Ali Gokhan Yavuz, Sirma Yavuz   +2 more
doaj   +1 more source

AndroMalPack: enhancing the ML-based malware classification by detection and removal of repacked apps for Android systems. [PDF]

Sci Rep, 2022
Rafiq H, Aslam N, Aleem M, Issac B, Randhawa RH.   +4 more
europepmc   +1 more source

Convolution neural network with batch normalization and inception-residual modules for Android malware classification. [PDF]

Sci Rep, 2022
Liu T, Zhang H, Long H, Shi J, Yao Y.
europepmc   +1 more source

Digital Forensics for Malware Classification: An Approach for Binary Code to Pixel Vector Transition. [PDF]

Comput Intell Neurosci, 2022
Naeem MR, Amin R, Alshamrani SS, Alshehri A.   +3 more
europepmc   +1 more source

Android malware classification based on random vector functional link and artificial Jellyfish Search optimizer. [PDF]

PLoS One, 2021
Elkabbash ET, Elkabbash ET, Mostafa RR, Barakat SI.   +3 more
europepmc   +1 more source

An adaptive neuro-fuzzy inference system for multinomial malware classification

Journal of Nigerian Society of Physical Sciences
Malware detection and classification are important requirements for information security because malware poses a great threat to computer users. As the growth of technology increases, malware is getting more sophisticated and thereby more difficult to ...
Amos Orenyi Bajeh, Mary Olayinka Olaoye, Fatima Enehezei Usman-Hamza, Ikeola Suhurat Olatinwo, Peter ogirima Sadiku, Abdulkadir Bolakale Sakariyah   +5 more
doaj   +1 more source

Multimodal Techniques for Malware Classification

The threat of malware is a serious concern for computer networks and systems, highlighting the need for accurate classification techniques. In this research, we experiment with multimodal machine learning approaches for malware classification, based on the structured nature of the Windows Portable Executable (PE) file format.
Jiang, Jonathan, Stamp, Mark
openaire   +3 more sources

DeepGray: Malware Classification Using Grayscale Images with Deep Learning

Proceedings of the International Florida Artificial Intelligence Research Society Conference
In the ever-evolving landscape of cybersecurity, the threat posed by malware continues to loom large, necessitating innovative and robust approaches for its effective detection and classification. In this paper, we introduce a novel method, DeepGray, for
Haodi Jiang, Harshitha Polsani, Yuexin Liu   +2 more
doaj   +1 more source