A comparative evaluation of novelty detection algorithms for discrete sequences

• Published in: arXiv.org
• Main Authors: Domingues, Rémi, Michiardi, Pietro, Barlet, Jérémie, Filippone, Maurizio
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 29.11.2019
• Subjects: Algorithms; Anomalies; Computer Science - Learning; Cybersecurity; Datasets; Fraud; Identification methods; Intrusion detection systems; Response time; Statistics - Machine Learning
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.1902.10940

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.