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Today it is possible to realize gigabit wireless links spanning across kilometers at a fraction of the cost of the wired equivalent. In the same period, mesh network evolved from being experimental tools confined into university labs, to systems running in several real world scenarios. Mesh networks can now provide city\u2010wide coverage and can compete on the market of Internet access. Yet, being wireless distributed networks, mesh networks are still hard to maintain and monitor. This paper explains how today we can perform monitoring, anomaly detection and root cause analysis in mesh networks using Big Data techniques. It first describes the architecture of a modern mesh network, it justifies the use of Big Data techniques and provides a design for the storage and analysis of Big Data produced by a large\u2010scale mesh network. While proposing a generic infrastructure, we focus on its application in the security domain.<\/jats:p>","DOI":"10.1002\/spy2.53","type":"journal-article","created":{"date-parts":[[2018,12,27]],"date-time":"2018-12-27T23:34:07Z","timestamp":1545953647000},"update-policy":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Big Data and machine learning approach for network monitoring and security"],"prefix":"10.1002","volume":"2","author":[{"ORCID":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/orcid.org\/0000-0002-5780-5008","authenticated-orcid":false,"given":"Leonardo","family":"Maccari","sequence":"first","affiliation":[{"name":"Department of Information Engineering and Computer Science University of Trento  Trento 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