Zero-Day Malware Detection and Effective Malware Analysis Using Shapley Ensemble Boosting and Bagging Approach

Zero-Day Malware Detection and Effective Malware Analysis Using Shapley Ensemble Boosting and Bagging Approach

Software products from all vendors have vulnerabilities that can cause a security concern. Malware is used as a prime exploitation tool to exploit these vulnerabilities. Machine learning (ML) methods are efficient in detecting malware and are state-of-art. The effectiveness of ML models can be augme...

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Bibliographic Details
Main Authors: Kumar, R. (Author), Subbiah, G. (Author)
Format: Article
Language:English
Published: MDPI 2022
Subjects:
Adaptive boosting
article
artificial intelligence
bagging
Bagging
boosting
Boosting
computer security
Computer security
Cybe se-curity
cyber se-curity
Cybersecurity
false negative result
False negatives
false positive result
Feature extraction
Forecasting
human
machine learning
Machine learning
Machine learning models
malware
Malware
Malware detection
prediction
probability
Shapley value
vulnerability
Zero day vulnerabilities
Zero-day attack
zero-day malware detection
Zero-day malware detection
zero-day vulnerability
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