Results 101 to 110 of about 5,558 (210)

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

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

Efficient malware detection using NLP and deep learning model

Alexandria Engineering Journal
Malware has emerged as a significant challenge in contemporary society, growing in tandem with technological advancements. Consequently, the classification of malware has become a pressing concern for various services.
Umesh Gupta, Shubham Kandpal, Hayam Alamro, Mashael M. Asiri, Meshari H. Alanazi, Ali M. Al-Sharafi, Shaymaa Sorour   +6 more
doaj   +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

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

AN EFFECTIVE MALWARE CLASSIFICATION METHOD BASED ON BYTE-TO-IMAGE TRANSFORMATION AND INTEGRATION OF THE VISION TRANSFORMER MODEL

Tạp chí Khoa học
Malware classification is a critical problem in cybersecurity, characterized by numerous challenges due to the complexity and diversity of malware variants. In this study, we propose a novel approach that transforms bytecode into image representations
Nguyen Thi Thu Thuy*, Do Thi Hong Linh, Hoang Thi Hong Ha, Pham Thi Cuc, Pham Anh Binh
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.
Grégoire Barrué, Tony Quertier
openaire   +2 more sources

Generating Synthetic Malware Samples Using Generative AI

IEEE Access
Malware 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, Kylie Trousil, Quang Duy Tran, Fabio Di Troia, Younghee Park   +4 more
doaj   +1 more source

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

Malware classification based on target location [PDF]

, 2014
The combination of Malicious and Software have contribute a phrase call as Malware. Malware are software that is intended to damage or disable computers and computer systems.
Nasuha, Noor Baha
core