- Title
- Real-time big data processing for anomaly detection : a survey
- Creator
- Ariyaluran Habeeb, Riyaz; Nasaruddin, Fariza; Gani, Abdullah; Targio Hashem, Ibrahim; Ahmed, Ejaz; Imran, Muhammad
- Date
- 2019
- Type
- Text; Journal article; Review
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/186116
- Identifier
- vital:16808
- Identifier
-
https://doi.org/10.1016/j.ijinfomgt.2018.08.006
- Identifier
- ISBN:0268-4012 (ISSN)
- Abstract
- The advent of connected devices and omnipresence of Internet have paved way for intruders to attack networks, which leads to cyber-attack, financial loss, information theft in healthcare, and cyber war. Hence, network security analytics has become an important area of concern and has gained intensive attention among researchers, off late, specifically in the domain of anomaly detection in network, which is considered crucial for network security. However, preliminary investigations have revealed that the existing approaches to detect anomalies in network are not effective enough, particularly to detect them in real time. The reason for the inefficacy of current approaches is mainly due the amassment of massive volumes of data though the connected devices. Therefore, it is crucial to propose a framework that effectively handles real time big data processing and detect anomalies in networks. In this regard, this paper attempts to address the issue of detecting anomalies in real time. Respectively, this paper has surveyed the state-of-the-art real-time big data processing technologies related to anomaly detection and the vital characteristics of associated machine learning algorithms. This paper begins with the explanation of essential contexts and taxonomy of real-time big data processing, anomalous detection, and machine learning algorithms, followed by the review of big data processing technologies. Finally, the identified research challenges of real-time big data processing in anomaly detection are discussed. © 2018 Elsevier Ltd
- Publisher
- Elsevier Ltd
- Relation
- International Journal of Information Management Vol. 45, no. (2019), p. 289-307
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2018 Elsevier Ltd
- Rights
- Open Access
- Subject
- 3503 Business systems in context; 4609 Information systems; 4610 Library and information studies; Anomaly detection and machine learning algorithms; Big data processing; Real-time
- Full Text
- Reviewed
- Hits: 944
- Visitors: 1423
- Downloads: 756
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | SOURCE1 | Accepted version | 1 MB | Adobe Acrobat PDF | View Details Download |