The Use of Machine Learning Techniques to Advance the Detection and Classification of Unknown Malware.
Ihab ShhadatBara BatainehAmena HayajnehZiad A. Al-SharifPublished in: ANT/EDI40 (2020)
Keyphrases
- machine learning
- machine learning methods
- classification accuracy
- classification systems
- detection accuracy
- decision trees
- detection method
- pattern recognition
- support vector machine
- machine learning algorithms
- robust detection
- pattern classification
- false positives
- machine learning approaches
- malware detection
- supervised learning
- mammogram images
- support vector
- spam detection
- feature extraction and classification
- neyman pearson
- malicious executables
- automatic classification
- microcalcification clusters
- benchmark datasets
- detection algorithm
- active learning
- discriminative classifiers
- data sets
- object recognition
- preprocessing
- digital mammograms
- anti virus
- image classification
- unsupervised learning
- training samples
- reverse engineering
- supervised learning algorithms
- support vector machine svm
- bayesian methods
- classification rules
- detection rate
- false alarms