A comparative evaluation of novelty detection algorithms for discrete sequences
This work addresses the need for effective anomaly detection in fields like intrusion detection and fraud prevention, but it is incremental as it focuses on comparative evaluation rather than introducing new methods.
The paper tackled the problem of identifying anomalies in discrete sequences by experimentally comparing state-of-the-art novelty detection methods, finding that certain methods are efficient and appropriate for specific use cases based on performance tests including scalability and memory usage.
The identification of anomalies in temporal data is a core component of numerous research areas such as intrusion detection, fault prevention, genomics and fraud detection. This article provides an experimental comparison of the novelty detection problem applied to discrete sequences. The objective of this study is to identify which state-of-the-art methods are efficient and appropriate candidates for a given use case. These recommendations rely on extensive novelty detection experiments based on a variety of public datasets in addition to novel industrial datasets. We also perform thorough scalability and memory usage tests resulting in new supplementary insights of the methods' performance, key selection criterion to solve problems relying on large volumes of data and to meet the expectations of applications subject to strict response time constraints.