Combining traditional machine learning and anomaly detection for several imbalanced Android malware dataset's classification.
Yiwei GanQian HanYumeng GaoPublished in: ICMLT (2022)
Keyphrases
- anomaly detection
- malware detection
- machine learning
- intrusion detection
- unsupervised learning
- machine learning methods
- text classification
- pattern recognition
- network anomaly detection
- anomalous behavior
- network intrusion detection
- network traffic
- machine learning algorithms
- support vector machine
- decision trees
- computer security
- network security
- feature selection
- intrusion detection system
- model selection
- supervised learning
- detect anomalies
- detecting anomalies
- feature vectors
- pattern classification
- class imbalance
- behavior analysis
- data mining
- data sets
- support vector
- computational intelligence
- text mining
- knowledge discovery
- normal behavior
- unsupervised anomaly detection
- one class support vector machines
- detecting anomalous
- cost sensitive
- labeled data
- maximum likelihood
- principal component analysis
- training set
- feature space
- object recognition
- learning algorithm