Results 111 to 120 of about 1,811,594 (209)

An ensemble approach for imbalanced multiclass malware classification using 1D-CNN. [PDF]

PeerJ Comput Sci, 2023
Panda B, Bisoyi SS, Panigrahy S.
europepmc   +1 more source

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

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

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

E liberaci dal malware

, 2022
PublishedGli strumenti tecnologici sono parte integrante della vita quotidiana e proprio per tale ragione è bene che vengano utilizzati in modo sicuro e consapevole. Lo scopo di questa introduzione all’educazione civica digitale è rendere consapevoli gli
RedOpen Factory
core  

Wavelet-Based and MAML-Driven Framework for Enhanced Few-Shot Malware Classification

Applied Sciences
Traditional malware classification approaches primarily address fixed sets of well-studied malware types and therefore struggle to accommodate the continual emergence of novel or previously unseen malware strains.
Abdullah Almuqrin, Ibrahim Mutambik, Majed Abusharhah   +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

Advancing Malware Classification With an Evolving Clustering Method

, 2018
This article describes how honeypots and intrusion detection systems serve as major mechanisms for security administrators to collect a variety of sample viruses and malware for further analysis, classification, and system protection.
Chia-Mei Chen, Shi-Hao Wang
core   +1 more source

A multimodal feature fusion-driven framework for IoT malware classification

Discover Computing
Rapid growth of Internet of Things (IoT) devices contributed to a significant surge in malware-based cyber-attacks. Traditional malware detection methods often struggle to adapt to the heterogeneous nature of IoT devices and the rapidly evolving threat ...
Sumit Kumar, Prachi, Jyoti Sahni
doaj   +1 more source