Results 91 to 100 of about 465,682 (204)

A Blockchain and Federated Learning Framework for Image-Based IoT Malware Detection and Prevention

IoT
Internet of Things (IoT) devices are increasingly targeted by rapidly evolving malware, yet collaborative detection remains challenged by privacy leakage, noisy and imbalanced training data, and weak integrity guarantees when sharing model updates.
Najem N. Sirhan, Riyad Alrousan, Hussam N. Fakhouri   +2 more
doaj   +1 more source

Robust Intelligent Malware Detection Using Deep Learning

IEEE Access, 2019
Security breaches due to attacks by malicious software (malware) continue to escalate posing a major security concern in this digital age. With many computer users, corporations, and governments affected due to an exponential growth in malware attacks ...
R. Vinayakumar, Mamoun Alazab, K. P. Soman, Prabaharan Poornachandran, Sitalakshmi Venkatraman   +4 more
doaj   +1 more source

Through the static: Demystifying malware visualization via explainability

Journal of Information Security and Applications
Security researchers grapple with the surge of malicious files, necessitating swift identification and classification of malware strains for effective protection. Visual classifiers and in particular Convolutional Neural Networks (CNNs) have emerged as vital tools for this task.
Brosolo, Matteo, P., Vinod, Conti, Mauro
openaire   +4 more sources

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

WinDroid: A Novel Framework for Windows and Android Malware Family Classification Using Hierarchical Ensemble Support Vector Machines With Multiview Handcrafted and Deep Learning Features

IET Information Security
The rapid growth and diversification of malware variants, driven by advanced code obfuscation, evasion, and antianalysis techniques, present a significant threat to cybersecurity.
K. Sundara Krishnan, S. Syed Suhaila
doaj   +1 more source

AFAgarap/malware-classification v0.1-alpha

, 2017
<p>Code implementation of "Towards Building an Intelligent Anti-Malware System: A Deep Learning Approach using Support Vector Machine (SVM) for Malware Classification"</p ...
Abien Fred Agarap
core   +1 more source

ARSNet: A Novel Malware Visualization Detection Method

IEEE Access
Malware detection remains a significant challenge in cybersecurity. To address this, visualization-based methods map binary code into image representations to leverage visual features for rapid identification.
Xinbo Liu, Wei Liu, Peng Nie, Zhuojun Fu
doaj   +1 more source

IMCMK-CNN: A lightweight convolutional neural network with Multi-scale Kernels for Image-based Malware Classification

Alexandria Engineering Journal
Rapid and accurate identification of unknown malware and its variants is the premise and basis for the effective prevention of malicious attacks. However, with the explosive growth of malware variants, the efficiency of manual updating of the sample ...
Dandan Zhang, Yafei Song, Qian Xiang, Yang Wang   +3 more
doaj   +1 more source

vinayakumarr/Android-Malware-Detection v1

, 2019
Android malware detection using static and dynamic ...
Vinayakumar R
core   +1 more source

Research on lightweight malware classification method based on image domain

Tongxin xuebao
To address the high deployment costs and long prediction times associated with traditional malware classification methods, a lightweight malware visualization classification method was proposed.
SUN Jingzhang, CHENG Yinan, ZOU Binghui, QIAO Tonghua, FU Sizheng, ZHANG Qi, CAO Chunjie   +6 more
doaj