A Machine-Learning Clustering Approach for Intrusion Detection to IoT Devices.
Stefanos PapafotikasAthanasios KakarountasPublished in: SEEDA-CECNSM (2019)
Keyphrases
- intrusion detection
- machine learning
- anomaly detection
- intrusion detection system
- network security
- data mining
- high detection rate
- network traffic
- clustering algorithm
- unsupervised learning
- network intrusion detection
- cyber security
- k means
- detecting anomalous
- data analysis
- artificial immune
- data mining techniques
- mobile devices
- information security
- false positives and false negatives
- feature selection
- computer security
- alert correlation
- text mining
- data points
- text classification
- active learning
- network intrusion
- object recognition
- pattern recognition
- computer vision
- network intrusion detection systems
- learning algorithm
- cloud computing
- fraud detection
- knowledge discovery
- network attacks
- intrusion prevention
- intrusion detection and prevention