Machine learning-based run-time anomaly detection in software systems: An industrial evaluation.
Fabian HuchMojdeh GolaghaAna PetrovskaAlexander KraussPublished in: MaLTeSQuE@SANER (2018)
Keyphrases
- anomaly detection
- software systems
- machine learning
- network anomaly detection
- intrusion detection
- source code
- software engineering
- detecting anomalies
- anomalous behavior
- network traffic
- network intrusion detection
- software maintenance
- unsupervised learning
- software development
- pattern recognition
- linux kernel
- intrusion detection system
- software evolution
- one class support vector machines
- network security
- cumulative sum
- negative selection algorithm
- artificial intelligence
- text mining
- data mining
- multi agent systems
- active learning
- probabilistic model
- software components
- relational databases
- pairwise
- support vector machine
- detect anomalies
- database systems
- feature selection
- supervised learning
- web applications
- databases
- clone detection
- software intensive systems
- data sets