Semi-supervised Trojan Nets Classification Using Anomaly Detection Based on SCOAP Features.
Pei-Yu LoChi-Wei ChenWei-Ting HsuChih-Wei ChenChin-Wei TienSy-Yen KuoPublished in: ISCAS (2022)
Keyphrases
- anomaly detection
- unsupervised anomaly detection
- semi supervised
- feature vectors
- classification accuracy
- feature set
- unsupervised learning
- feature extraction
- detecting anomalies
- feature space
- intrusion detection
- network intrusion detection
- intrusion detection system
- svm classifier
- network traffic
- support vector
- behavior analysis
- supervised learning
- detecting anomalous
- network anomaly detection
- anomalous behavior
- decision trees
- machine learning
- network security
- pattern recognition
- one class support vector machines
- malware detection
- computer security
- negative selection algorithm
- network intrusion
- detect anomalies
- pattern classification
- unlabeled data
- active learning
- support vector machine
- semi supervised learning