Real-time big data processing for anomaly detection: A Survey

作     者：Habeeb, Riyaz Ahamed Ariyaluran Nasaruddin, Fariza Gani, Abdullah Hashem, Ibrahim Abaker Targio Ahmed, Ejaz Imran, Muhammad 

作者机构：Univ Malaya Fac Comp Sci & Informat Technol Dept Informat Syst Kuala Lumpur 50603 Malaysia Taylors Univ Sch Comp & Informat Technol Subang Jaya 47500 Selangor Malaysia Univ Malaya Ctr Res Mobile Cloud Comp Kuala Lumpur Malaysia King Saud Univ Coll Appl Comp Sci Riyadh Saudi Arabia 

出 版 物：《INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT》 (国际信息管理杂志)

年 卷 期：2019年第45卷

页      面：289-307页

核心收录：

学科分类：0303[法学-社会学] 1205[管理学-图书情报与档案管理] 03[法学] 

基　　金：Deanship of Scientific Research at King Saud University [1435-051] 

主　　题：Real-time Big data processing Anomaly detection and machine learning algorithms 

摘      要：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.