01883nas a2200313   4500008004100000022001400041245006200055210006100117260004200178300001000220490000600230520100200236653002501238653001501263653001801278653001501296653002401311653002301335653002401358653002101382653002301403653001501426653001301441653002701454100001901481700001701500700001601517856003601533       2016                        eng d  a1927-032100aIntrusion Learning: An Overview of an Emergent Discipline0 aIntrusion Learning An Overview of an Emergent Discipline  aOttawabTalent First Networkc02/2016  a15-200 v63 aThe purpose of this article is to provide a definition of intrusion learning, identify its distinctive aspects, and provide recommendations for advancing intrusion learning as a practice domain. The authors define intrusion learning as the collection of online network algorithms that learn from and monitor streaming network data resulting in effective intrusion-detection methods for enabling the security and resiliency of enterprise systems. The network algorithms build on advances in cyber-defensive and cyber-offensive capabilities. Intrusion learning is an emerging domain that draws from machine learning, intrusion detection, and streaming network data. Intrusion learning offers to significantly enhance enterprise security and resiliency through augmented perimeter defense and may mitigate increasing threats facing enterprise perimeter protection. The article will be of interest to researchers, sponsors, and entrepreneurs interested in enhancing enterprise security and resiliency.10aadversarial learning10aclustering10acybersecurity10aenterprise10aintrusion detection10aintrusion learning10alearning algorithms10amachine learning10areal-time analysis10aresiliency10asecurity10astreaming network data1 aBailetti, Tony1 aGad, Mahmoud1 aShah, Ahmed  uhttp://timreview.ca/article/964