A survey on malware detection using data mining techniques

Y Ye, T Li, D Adjeroh, SS Iyengar - ACM Computing Surveys (CSUR), 2017 - dl.acm.org
In the Internet age, malware (such as viruses, trojans, ransomware, and bots) has posed
serious and evolving security threats to Internet users. To protect legitimate users from these …

[HTML][HTML] Malware analysis and classification: A survey

E Gandotra, D Bansal, S Sofat - Journal of Information Security, 2014 - scirp.org
One of the major and serious threats on the Internet today is malicious software, often
referred to as a malware. The malwares being designed by attackers are polymorphic and …

A survey on machine learning techniques for cyber security in the last decade

K Shaukat, S Luo, V Varadharajan, IA Hameed… - IEEE …, 2020 - ieeexplore.ieee.org
Pervasive growth and usage of the Internet and mobile applications have expanded
cyberspace. The cyberspace has become more vulnerable to automated and prolonged …

Generating adversarial malware examples for black-box attacks based on GAN

W Hu, Y Tan - International Conference on Data Mining and Big Data, 2022 - Springer
Abstract Machine learning has been used to detect new malware in recent years, while
malware authors have strong motivation to attack such algorithms. Malware authors usually …

[PDF][PDF] Learning to detect and classify malicious executables in the wild.

JZ Kolter, MA Maloof - Journal of Machine Learning Research, 2006 - jmlr.org
We describe the use of machine learning and data mining to detect and classify malicious
executables as they appear in the wild. We gathered 1,971 benign and 1,651 malicious …

Intelligent vision-based malware detection and classification using deep random forest paradigm

SA Roseline, S Geetha, S Kadry, Y Nam - IEEE Access, 2020 - ieeexplore.ieee.org
Malware is a rapidly increasing menace to modern computing. Malware authors continually
incorporate various sophisticated features like code obfuscations to create malware variants …