Outlier detection techniques for big data streams: focus on cyber security
| Autor: | Ayoub Ait Lahcen, Fatima Zahra Benjelloun, Samir Belfkih |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
Data stream mining business.industry Computer Networks and Communications Dynamic data Big data 020206 networking & telecommunications Context (language use) 02 engineering and technology Information security Computer security computer.software_genre Computer Science Applications Scalability Outlier 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Anomaly detection business computer |
| Zdroj: | International Journal of Internet Technology and Secured Transactions. 9:446 |
| ISSN: | 1748-5703 1748-569X |
| DOI: | 10.1504/ijitst.2019.102799 |
| Popis: | In recent years, detecting outliers in big data streams has become a main challenge in several domains (e.g., medical monitoring, government security, information security, natural disasters, and online financial frauds). In fact, unlike regular static data, streams raise many issues like high multidimensionality, dynamic data distribution, unpredictable relationships, data sequences, uncertainty and transiency. Most of the proposed approaches can handle some of these issues but not all. In addition, they provide limited considerations with regard to scalability and performance. Real-world applications require high performance, resources optimisation and real-time responsiveness when detecting outliers. This is useful to extract knowledge, detect incidents and predict patterns changes. In this paper, we review and compare recent studies in detecting outliers for data streams. We investigate how researchers improved the outcome of different models and monitoring systems, especially in the context of cyber security. |
| Databáze: | OpenAIRE |
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