Results 91 to 100 of about 31,501 (227)

Mi-maml: classifying few-shot advanced malware using multi-improved model-agnostic meta-learning

Cybersecurity
Malware classification has been successful in utilizing machine learning methods. However, it is limited by the reliance on a large number of high-quality labeled datasets and the issue of overfitting. These limitations hinder the accurate classification
Yulong Ji, Kunjin Zou, Bin Zou
doaj   +1 more source

GRASE: Granulometry Analysis With Semi Eager Classifier to Detect Malware

International Journal of Interactive Multimedia and Artificial Intelligence
Technological advancement in communication leading to 5G, motivates everyone to get connected to the internet including ‘Devices’, a technology named Web of Things (WoT).
Mahendra Deore, Manoj Tarambale, Jambi Ratna Raja Kumar, Sachin Sakhare   +3 more
doaj   +1 more source

AndroDFA: Android Malware Classification Based on Resource Consumption [PDF]

, 2020
Luca Massarelli, Leonardo Aniello, Claudio Ciccotelli, Leonardo Querzoni, Daniele Ucci, Roberto Baldoni   +5 more
openalex   +1 more source

MalGEA: A malware analysis framework via matrix factorization based node embedding and graph external attention

Array
As one of the major threats in cybersecurity, malware has been growing continuously and steadily. In recent years, researchers have proposed a number of graph representation learning based malware detection methods by leveraging the intrinsic topological
Ruisheng Li, Qilong Zhang, Huimin Shen
doaj   +1 more source

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  

An empirical study of problems and evaluation of IoT malware classification label sources

Journal of King Saud University: Computer and Information Sciences
With the proliferation of malware on IoT devices, research on IoT malicious code has also become more mature. Most studies use learning models to detect or classify malware.
Tianwei Lei, Jingfeng Xue, Yong Wang, Thar Baker, Zequn Niu   +4 more
doaj   +1 more source

Image-based Malware Classification: A Space Filling Curve Approach

2019 IEEE Symposium on Visualization for Cyber Security (VizSec), 2019
Anti-virus (AV) software is effective at distinguishing between benign and malicious programs yet lack the ability to effectively classify malware into their respective family classes. AV vendors receive considerably large volumes of malicious programs daily and so classification is crucial to quickly identify variants of existing malware that would ...
openaire   +3 more sources

Why an Android App is Classified as Malware? Towards Malware Classification Interpretation [PDF]

, 2020
Bozhi Wu, Sen Chen, Cuiyun Gao, Lingling Fan, Yang Liu, Weiping Wen, Michael R. Lyu   +6 more
openalex   +1 more source

Exploring Timeline-Based Malware Classification [PDF]

, 2013
Over the decades or so, Anti-Malware (AM) communities have been faced with a substantial increase in malware activity, including the development of ever-more-sophisticated methods of evading detection. Researchers have argued that an AM strategy which is successful in a given time period cannot work at a much later date due to the changes in malware ...
Islam, Rafiqul, Altas, Irfan, Islam, Md Saiful   +2 more
openaire   +3 more sources

LDAM: A lightweight dual attention module for optimizing automotive malware classification

Array
In 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