Results 101 to 110 of about 1,811,594 (209)
Adaptive Semantics-Aware Malware Classification
, 2016 Automatic malware classification is an essential improvement over the widely-deployed detection procedures using manual signatures or heuristics. Although there exists an abundance of methods for collecting static and behavioral malware data, there is a ...
Lengyel, Tamas +9 more
core +1 more source
ArrayAs 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 SciencesWith 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 +4 more
doaj +1 more source
Tạp chí Khoa họcMalware 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 +3 more sources
Humans vs. Machines in Malware Classification
, 2022 International audienceToday, the classification of a file as either benign or malicious is performed by a combination of deterministic indicators (such as antivirus rules), Machine Learning classifiers, and, more importantly, the judgment of human ...
Han, Yufei +3 more
core
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
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
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 +2 more sources
Malware Visualization for Fine-Grained Classification
IEEE Access, 2018 Due to the rapid rise of automated tools, the number of malware variants has increased dramatically, which poses a tremendous threat to the security of the Internet. Recently, some methods for quick analysis of malware have been proposed, but these methods usually require a large computational overhead and cannot classify samples accurately for large ...
Jianwen Fu +4 more
openaire +3 more sources