Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Machine Learning for CybersecurityMalware Anomaly Detection Using Local Outlier Factor Technique

Machine Learning for Cybersecurity: Malware Anomaly Detection Using Local Outlier Factor Technique [Malware anomaly detection is a major research area as new variants of malware continue to wreak havoc on business organizations. In this study, we propose a new technique based on the Local outlier factor algorithm to detect anomalous malware behavior. We empirically validate the performance and effectiveness of our technique on real-world datasets. This is an efficient technique for malware detection as the model trained for this purpose is based on unsupervised learning. The model trains on the anomalies, that is, the unusual behavior in a process, making it significantly effective.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Machine Learning for CybersecurityMalware Anomaly Detection Using Local Outlier Factor Technique

Download PDF
11 pages

Loading next page...
 
/lp/springer-journals/machine-learning-for-cybersecurity-malware-anomaly-detection-using-9E07T4GgTV
Publisher
Springer International Publishing
Copyright
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
ISBN
978-3-031-15892-6
Pages
37 –48
DOI
10.1007/978-3-031-15893-3_3
Publisher site
See Chapter on Publisher Site

Abstract

[Malware anomaly detection is a major research area as new variants of malware continue to wreak havoc on business organizations. In this study, we propose a new technique based on the Local outlier factor algorithm to detect anomalous malware behavior. We empirically validate the performance and effectiveness of our technique on real-world datasets. This is an efficient technique for malware detection as the model trained for this purpose is based on unsupervised learning. The model trains on the anomalies, that is, the unusual behavior in a process, making it significantly effective.]

Published: Sep 24, 2022

Keywords: Local outlier factor; Outlier detection; Anomaly detection; Malware detection; Malware dataset; Dataset validation

There are no references for this article.