Applying both positive and negative selection to supervised learning for anomaly detection.
Xiaoshu HangHonghua DaiPublished in: GECCO (2005)
Keyphrases
- positive and negative
- anomaly detection
- supervised learning
- unsupervised learning
- intrusion detection
- detecting anomalies
- network intrusion detection
- decision rules
- positive or negative
- network traffic
- anomalous behavior
- novelty detection
- intrusion detection system
- learning tasks
- reinforcement learning
- detect anomalies
- detecting anomalous
- network anomaly detection
- unsupervised anomaly detection
- semi supervised
- one class support vector machines
- unlabeled data
- network security
- labeled data
- learning problems
- selection strategy
- positive examples
- active learning
- training set
- negative selection algorithm
- network intrusion
- data sets
- neural network
- real world
- learning algorithm
- image segmentation
- training data
- semi supervised learning