2020/05/23 by Fadi Salo, Salo, Fadi, MohammadNoor Injadat +5
Computer Science · #Advanced Malware Detection Techniques #Anomaly Detection Techniques and Applications #Artificial Intelligence (cs.AI) #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Network Security and Intrusion Detection
paper · pdf · doi:10.48550/arxiv.2005.12267
openalex publication_date 2020/05/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Cloud computing has become a powerful and indispensable technology for\ncomplex, high performance and scalable computation. The exponential expansion\nin the deployment of cloud technology has produced a massive amount of data\nfrom a variety of applications, resources and platforms. In turn, the rapid\nrate and volume of data creation has begun to pose significant challenges for\ndata management and security. The design and deployment of intrusion detection\nsystems (IDS) in the big data setting has, therefore, become a topic of\nimportance. In this paper, we conduct a systematic literature review (SLR) of\ndata mining techniques (DMT) used in IDS-based solutions through the period\n2013-2018. We employed criterion-based, purposive sampling identifying 32\narticles, which constitute the primary source of the present survey. After a\ncareful investigation of these articles, we identified 17 separate DMTs\ndeployed in an IDS context. This paper also presents the merits and\ndisadvantages of the various works of current research that implemented DMTs\nand distributed streaming frameworks (DSF) to detect and/or prevent malicious\nattacks in a big data environment.\n