IMPLEMENTASI MODEL DEEP LEARNING INCEPTION RESNET-V2 UNTUK IMAGE CLASSIFICATION PADA MALWARE [PDF]
Image malware classification is a technique used to identify and classify types of malware using images generated by dynamic and static analysis. The most commonly used method for image malware classification is Convolutional Neural Network (CNN) trained
Djawas, Jafar Shodiq
core
LDAM: A lightweight dual attention module for optimizing automotive malware classification
ArrayIn recent years, electric vehicles have become prime targets for cyberattacks, with attackers exploiting public charging stations, USB ports, and other entry points to implant malware. This can lead to network outages and power disruptions.
Jiahui Chen, Mingrui Wu, Huiwu Huang
doaj +1 more source
Generating Synthetic Malware Samples Using Generative AI
IEEE AccessMalware attacks have a significant negative impact on organizations of varied scales in the field of cybersecurity. Recently, malware researchers have increasingly turned to machine learning techniques to combat sophisticated obfuscation methods used in ...
Tiffany Bao +4 more
doaj +1 more source
Quantum Machine Learning for Malware Classification
In a context of malicious software detection, machine learning (ML) is widely used to generalize to new malware. However, it has been demonstrated that ML models can be fooled or may have generalization problems on malware that has never been seen. We investigate the possible benefits of quantum algorithms for classification tasks.
Barrué, Grégoire, Quertier, Tony
openaire +2 more sources
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
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 AccessMalware 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 +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 +4 more
europepmc +1 more source
Wavelet-Based and MAML-Driven Framework for Enhanced Few-Shot Malware Classification
Applied SciencesTraditional 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 +2 more
doaj +1 more source