Instance-Based Learning Algorithms
Machine Learning
Original Contribution: Stacked generalization
Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Rule Induction with CN2: Some Recent Improvements
EWSL '91 Proceedings of the European Working Session on Machine Learning
Classification by Voting Feature Intervals
ECML '97 Proceedings of the 9th European Conference on Machine Learning
Data Mining Methods for Detection of New Malicious Executables
SP '01 Proceedings of the 2001 IEEE Symposium on Security and Privacy
A machine learning approach to detecting attacks by identifying anomalies in network traffic
A machine learning approach to detecting attacks by identifying anomalies in network traffic
Recent worms: a survey and trends
Proceedings of the 2003 ACM workshop on Rapid malcode
N-Gram-Based Detection of New Malicious Code
COMPSAC '04 Proceedings of the 28th Annual International Computer Software and Applications Conference - Workshops and Fast Abstracts - Volume 02
Intrusion detection using an ensemble of intelligent paradigms
Journal of Network and Computer Applications - Special issue on computational intelligence on the internet
Comparison of feature selection and classification algorithms in identifying malicious executables
Computational Statistics & Data Analysis
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Learning to Detect and Classify Malicious Executables in the Wild
The Journal of Machine Learning Research
Detection of unknown computer worms based on behavioral classification of the host
Computational Statistics & Data Analysis
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Collective-agreement-based pruning of ensembles
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
Troika - An improved stacking schema for classification tasks
Information Sciences: an International Journal
The use of artificial intelligence based techniques for intrusion detection: a review
Artificial Intelligence Review
"Andromaly": a behavioral malware detection framework for android devices
Journal of Intelligent Information Systems
Mal-ID: automatic malware detection using common segment analysis and meta-features
The Journal of Machine Learning Research
Improving malware classification: bridging the static/dynamic gap
Proceedings of the 5th ACM workshop on Security and artificial intelligence
Using low-level dynamic attributes for malware detection based on data mining methods
MMM-ACNS'12 Proceedings of the 6th international conference on Mathematical Methods, Models and Architectures for Computer Network Security: computer network security
A comparative study of malware family classification
ICICS'12 Proceedings of the 14th international conference on Information and Communications Security
Information Sciences: an International Journal
Editorial: Guest editorial: Special issue on data mining for information security
Information Sciences: an International Journal
Review: Classification of malware based on integrated static and dynamic features
Journal of Network and Computer Applications
Detecting machine-morphed malware variants via engine attribution
Journal in Computer Virology
Malware detection by pruning of parallel ensembles using harmony search
Pattern Recognition Letters
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Detection of malicious software (malware) using machine learning methods has been explored extensively to enable fast detection of new released malware. The performance of these classifiers depends on the induction algorithms being used. In order to benefit from multiple different classifiers, and exploit their strengths we suggest using an ensemble method that will combine the results of the individual classifiers into one final result to achieve overall higher detection accuracy. In this paper we evaluate several combining methods using five different base inducers (C4.5 Decision Tree, Naive Bayes, KNN, VFI and OneR) on five malware datasets. The main goal is to find the best combining method for the task of detecting malicious files in terms of accuracy, AUC and Execution time.