Anomaly Detection in Aluminium Production with Unsupervised Machine Learning Classifiers.
Nikolaos KolokasThanasis VafeiadisDimosthenis IoannidisDimitrios TzovarasPublished in: INISTA (2019)
Keyphrases
- anomaly detection
- machine learning
- supervised classification
- unsupervised learning
- one class support vector machines
- unsupervised anomaly detection
- machine learning algorithms
- machine learning methods
- intrusion detection
- supervised learning
- novelty detection
- network anomaly detection
- decision trees
- detecting anomalies
- feature selection
- network traffic
- network intrusion detection
- pattern recognition
- support vector
- anomalous behavior
- computer security
- training data
- intrusion detection system
- training set
- negative selection algorithm
- learning algorithm
- semi supervised
- neural network
- semi supervised learning
- behavior analysis
- text mining
- natural language processing
- active learning
- detecting anomalous
- self organizing maps
- learning problems
- data mining
- computer vision
- reinforcement learning
- expectation maximization
- network intrusion
- connectionist systems