Visible to the public Intelligent Malware Detection Using Oblique Random Forest Paradigm

TitleIntelligent Malware Detection Using Oblique Random Forest Paradigm
Publication TypeConference Paper
Year of Publication2018
AuthorsRoseline, S. A., Geetha, S.
Conference Name2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI)
Keywordsbehavior-based detection techniques, classification accuracy, comprehensive malware detection, computerized online applications, decision tree learning models, Decision trees, false positive rate, feature extraction, Forestry, Human Behavior, intelligent malware detection, invasive software, learning (artificial intelligence), machine learning, machine learning solutions, malware behavior, malware classification, malware classification datasets, Metrics, Oblique Random Forest, oblique random forest ensemble learning technique, oblique random forest paradigm, pattern classification, privacy, pubcrawl, resilience, Resiliency, security community, signature-based detection techniques, stealthy malware, Support vector machines, Trojan horses, unknown malware
AbstractWith the increase in the popularity of computerized online applications, the analysis, and detection of a growing number of newly discovered stealthy malware poses a significant challenge to the security community. Signature-based and behavior-based detection techniques are becoming inefficient in detecting new unknown malware. Machine learning solutions are employed to counter such intelligent malware and allow performing more comprehensive malware detection. This capability leads to an automatic analysis of malware behavior. The proposed oblique random forest ensemble learning technique is efficient for malware classification. The effectiveness of the proposed method is demonstrated with three malware classification datasets from various sources. The results are compared with other variants of decision tree learning models. The proposed system performs better than the existing system in terms of classification accuracy and false positive rate.
Citation Keyroseline_intelligent_2018