Title: Outlier detection techniques for big data streams: focus on cyber security

Authors: Fatima-Zahra Benjelloun; Ayoub Ait Lahcen; Samir Belfkih

Addresses: LGS, ENSA, Ibn Tofail University, Kenitra, Morocco ' LGS, ENSA, Ibn Tofail University, Kenitra, Morocco; LRIT, Unité associée au CNRST URAC 29, Mohammed V University, Rabat, Morocco ' LGS, ENSA, Ibn Tofail University, Kenitra, Morocco

Abstract: 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.

Keywords: outlier detection; data streams; streaming; big data; high dimension; cyber security.

DOI: 10.1504/IJITST.2019.102799

International Journal of Internet Technology and Secured Transactions, 2019 Vol.9 No.4, pp.446 - 474

Received: 02 May 2017
Accepted: 17 Aug 2017
Published online: 08 Oct 2019 *

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