RAMD: registry-based anomaly malware detection using one-class ensemble classifiers

Asghar Tajoddin; Mahdi Abadi

doi:10.1007/s10489-018-01405-0

ScienceGate Book Chapters

JOURNAL ARTICLE

RAMD: registry-based anomaly malware detection using one-class ensemble classifiers

Asghar Tajoddin Mahdi Abadi

Year: 2019 Journal: Applied Intelligence Vol: 49 (7)Pages: 2641-2658 Publisher: Springer Science+Business Media

DOI: 10.1007/s10489-018-01405-0

Get Full-Text PDF Get Analytical Report

Keywords:

Computer science Malware Machine learning Artificial intelligence Classifier (UML) Cascading classifiers Support vector machine Ensemble learning Data mining Random subspace method Pattern recognition (psychology)

Metrics

Cited By

2.96

FWCI (Field Weighted Citation Impact)

Refs

0.92

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Advanced Malware Detection Techniques

Physical Sciences → Computer Science → Signal Processing

Network Security and Intrusion Detection

Physical Sciences → Computer Science → Computer Networks and Communications

Anomaly Detection Techniques and Applications

Physical Sciences → Computer Science → Artificial Intelligence

RAMD: registry-based anomaly malware detection using one-class ensemble classifiers

Metrics

Citation History

Topics

Related Documents

Video Anomaly Detection Using Ensemble One-Class Classifiers

AMD-EC: Anomaly-based Android malware detection using ensemble classifiers

One-class classifiers ensemble based anomaly detection scheme for process control systems

Using an Ensemble of One-Class SVM Classifiers to Harden Payload-based Anomaly Detection Systems

Anomaly Based Network Intrusion Detection Using Ensemble Classifiers