@article {964,
	title = {Intrusion Learning: An Overview of an Emergent Discipline},
	journal = {Technology Innovation Management Review},
	volume = {6},
	year = {2016},
	month = {02/2016},
	pages = {15-20},
	publisher = {Talent First Network},
	address = {Ottawa},
	abstract = {The 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.},
	keywords = {adversarial learning, clustering, cybersecurity, enterprise, intrusion detection, intrusion learning, learning algorithms, machine learning, real-time analysis, resiliency, security, streaming network data},
	issn = {1927-0321},
	doi = {http://doi.org/10.22215/timreview/964},
	url = {http://timreview.ca/article/964},
	author = {Tony Bailetti and Mahmoud Gad and Ahmed Shah}
}
