Data Mining with Big Data in Intrusion Detection Systems: A Systematic Literature Review

• URL: http://arxiv.org/abs/2005.12267v1
• Date: Sat, 23 May 2020 20:57:12 GMT
• Title: Data Mining with Big Data in Intrusion Detection Systems: A Systematic Literature Review
• Authors: Fadi Salo, MohammadNoor Injadat, Ali Bou Nassif, Aleksander Essex
• Abstract summary: Cloud computing has become a powerful and indispensable technology for complex, high performance and scalable computation. The rapid rate and volume of data creation has begun to pose significant challenges for data management and security. The design and deployment of intrusion detection systems (IDS) in the big data setting has, therefore, become a topic of importance.
• Score: 68.15472610671748
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cloud computing has become a powerful and indispensable technology for complex, high performance and scalable computation. The exponential expansion in the deployment of cloud technology has produced a massive amount of data from a variety of applications, resources and platforms. In turn, the rapid rate and volume of data creation has begun to pose significant challenges for data management and security. The design and deployment of intrusion detection systems (IDS) in the big data setting has, therefore, become a topic of importance. In this paper, we conduct a systematic literature review (SLR) of data mining techniques (DMT) used in IDS-based solutions through the period 2013-2018. We employed criterion-based, purposive sampling identifying 32 articles, which constitute the primary source of the present survey. After a careful investigation of these articles, we identified 17 separate DMTs deployed in an IDS context. This paper also presents the merits and disadvantages of the various works of current research that implemented DMTs and distributed streaming frameworks (DSF) to detect and/or prevent malicious attacks in a big data environment.

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