Results 71 to 80 of about 1,405 (158)

DaViz: Visualization for Android Malware Datasets

, 2022
With millions of Android malware samples available, researchers have a large amount of data to perform malware detection and classification, specially with the help of machine learning. Thus far, visualization tools focus on single samples or one-to-many comparison, but not a many-to-many approach.
Concepción Miranda, Tomás, Lalande, Jean-François, Viet Triem Tong, Valérie, Wilke, Pierre   +3 more
openaire   +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

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

On Visual Hallmarks of Robustness to Adversarial Malware

, 2018
A central challenge of adversarial learning is to interpret the resulting hardened model. In this contribution, we ask how robust generalization can be visually discerned and whether a concise view of the interactions between a hardened decision map and input samples is possible.
Huang, Alex, Al-Dujaili, Abdullah, Hemberg, Erik, O'Reilly, Una-May   +3 more
openaire   +2 more sources

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  

Application of deep learning in malware detection: a review

Journal of Big Data
The defense of malware remains an important research hotspot in the field of cyberspace security. Recognizing its profound research significance, our defense against malware is still an important research hotspot in the field of cyberspace security ...
Yafei Song, Dandan Zhang, Jian Wang, Yanan Wang, Yang Wang, Peng Ding   +5 more
doaj   +1 more source

A Survey on Visualization-Based Malware Detection

Journal of Cyber Security, 2022
Ahmad Moawad, Ahmed Ismail Ebada, Aya M. Al-Zoghby   +2 more
openaire   +1 more source

A dataset of windows malware execution traces. [PDF]

Data Brief
Raducu R, Villagrasa-Labrador A, Rodríguez RJ, Álvarez P.   +3 more
europepmc   +1 more source

Enhancing security in IoMT using federated TinyGAN for lightweight and accurate malware detection. [PDF]

Sci Rep
S D, Shankar MG, Daniel E, R BGV.
europepmc   +1 more source

A hierarchical deep learning framework with doubly regularized loss for robust malware detection and family categorization. [PDF]

Sci Rep
Abed Alsaedi S, Alwateer M, Helal Alharbi N, Magdy Balaha H, M Aboelnaga M, Badawy M, A Elhosseini M.   +6 more
europepmc   +1 more source