%0 Journal Article %J Technology Innovation Management Review %D 2016 %T Intrusion Learning: An Overview of an Emergent Discipline %A Tony Bailetti %A Mahmoud Gad %A Ahmed Shah %K adversarial learning %K clustering %K cybersecurity %K enterprise %K intrusion detection %K intrusion learning %K learning algorithms %K machine learning %K real-time analysis %K resiliency %K security %K streaming network data %X 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. %B Technology Innovation Management Review %I Talent First Network %C Ottawa %V 6 %P 15-20 %8 02/2016 %G eng %U http://timreview.ca/article/964 %N 2 %1 Carleton University Tony Bailetti is an Associate Professor in the Sprott School of Business and the Department of Systems and Computer Engineering at Carleton University, Ottawa, Canada. Professor Bailetti is the Director of Carleton University's Technology Innovation Management (TIM) program. His research, teaching, and community contributions support technology entrepreneurship, regional economic development, and international co-innovation. %2 VENUS Cybersecurity Corporation Mahmoud M. Gad is a Research Associate at VENUS Cybersecurity. He holds a PhD in Electrical and Computer Engineering from the University of Ottawa in Canada. Additionally, he holds an MSc in Electrical and Computer Engineering from the University of Maryland in College Park, United States. His research interests include cybercrime markets, machine learning for intrusion detection, analysis of large-scale networks, and cognitive radio networks. %3 Carleton University Ahmed Shah holds a BEng in Software Engineering and is pursuing an MASc degree in Technology Innovation Management at Carleton University in Ottawa, Canada. Ahmed has experience working in cybersecurity research with the VENUS Cybersecurity Corporation and has experience managing legal deliverables at IBM. %R http://doi.org/10.22215/timreview/964