Filtering Techniques for Rapid User Classification
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Summary
A study of the use of noise suppression filters as componants of a learning classification system for anomaly detection and finds that the median filter is generally to be preferred for this domain.
- Type
- article
- Published
- 1998-01-01
- Cited by
- 8
- References
- 13
- OpenAlex
- https://openalex.org/W24313
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:13584179
Keywords
Filter (signal processing), Window (computing), Computer science, Artificial intelligence, Detector
References
- Sequence Matching and Learning in Anomaly Detection for Computer Security
- Detecting the Abnormal: Machine Learning in Computer Security
- Classification and detection of computer intrusions
- Learning Patterns from Unix Process Execution Traces for Intrusion Detection
- Discrete-Time Signal Pro-cessing
- An Intrusion-Detection Model
- The Cuckoo's Egg
- Approaches to Online Learning and Concept Drift for User Identification in Computer Security
- A sense of self for Unix processes
- Computer security threat monitoring and surveillance
- Journal of Graph Algorithms and Applications Many-to-one Boundary Labeling
Cited by
- Temporal sequence learning and data reduction for anomaly detection
- A Constraint Programming Approach for Enumerating Motifs in a Sequence
- Extending modern SAT solvers for models enumeration
- Temporal sequence learning and data reduction for anomaly detection
- An Empirical Study of Two Approaches to Sequence Learning for Anomaly Detection
- Mining Top-k motifs with a SAT-based framework
- A SAT-Based Approach for Discovering Frequent, Closed and Maximal Patterns in a Sequence
- Model Generation for an Intrusion Detection System Using Genetic Algorithms
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