<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ahmed Shah</style></author><author><style face="normal" font="default" size="100%">Ibrahim Abualhaol</style></author><author><style face="normal" font="default" size="100%">Mahmoud Gad</style></author><author><style face="normal" font="default" size="100%">Michael Weiss</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Combining Exploratory Analysis and Automated Analysis for Anomaly Detection in Real-Time Data Streams</style></title><secondary-title><style face="normal" font="default" size="100%">Technology Innovation Management Review</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">anomaly detection</style></keyword><keyword><style  face="normal" font="default" size="100%">cybersecurity</style></keyword><keyword><style  face="normal" font="default" size="100%">exploratory analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">real-time data streams</style></keyword><keyword><style  face="normal" font="default" size="100%">visualization</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">04/2017</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://timreview.ca/article/1068</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Talent First Network</style></publisher><pub-location><style face="normal" font="default" size="100%">Ottawa</style></pub-location><volume><style face="normal" font="default" size="100%">7</style></volume><pages><style face="normal" font="default" size="100%">25-31</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Security analysts can become overwhelmed with monitoring real-time security information that is important to help them defend their network. They also tend to focus on a limited portion of the alerts, and therefore risk missing important events and links between them. At the heart of the problem is the system that analysts use to detect, explore, and respond to cyber-attacks. Developers of security analysis systems face the challenge of developing a system that can present different sources of information at multiple levels of abstraction, while also creating a system that is intuitive to use. In this article, we examine the complementary nature of exploratory analysis and automated analysis by testing the development of a system that monitors real-time Border Gateway Protocol (BGP) traffic for anomalies that might indicate security threats. BGP is an essential component for supporting the infrastructure of the Internet; however, it is also highly vulnerable and can be hijacked by attackers to propagate spam or launch denial-of-service attacks. Some of the attack scenarios on the BGP infrastructure can be quite elaborate, and it is difficult, if not impossible, to fully automate the detection of such attacks. This article makes two contributions: i) it describes a prototype platform for computing indicators and threat alerts in real time and for visualizing the context of an alert, and ii) it discusses the interaction of exploratory analysis (visualization) and automated analysis. This article is relevant to students, security researchers, and developers who are interested in the development or use of real-time security monitoring systems. They will gain insights into the complementary aspects of automated analysis and exploratory analysis through the development of a real-time streaming system.</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue><custom1><style face="normal" font="default" size="100%">VENUS Cybersecurity Corporation
Ahmed Shah holds a BEng in Software Engineering from Lakehead University in Thunder Bay, Canada, and a MEng in Technology Innovation Management from Carleton University in Ottawa, Canada. Ahmed has experience working in a wide variety of research roles at the VENUS Cybersecurity Corporation, the Global Cybersecurity Resource, and Carleton University.</style></custom1><custom2><style face="normal" font="default" size="100%">Carleton University
Ibrahim Abualhaol is a Research Scientist at Larus Technologies and an Adjunct Professor at Carleton University in Ottawa, Canada. He holds a BSc, an MSc, and a PhD in Electrical and Computer Engineering. He is a senior member of IEEE and a Professional Engineer (P.Eng) in Ontario, Canada. His research interests include real-time big-data analytics and its application in cybersecurity and wireless communication systems.</style></custom2><custom3><style face="normal" font="default" size="100%">VENUS Cybersecurity Corporation
Mahmoud M. Gad is a Research Scientist at the VENUS Cybersecurity Corporation. He holds a PhD in Electrical and Computer Engineering from the University of Ottawa in Canada. Additionally, he holds an MSc in ECE from the University of Maryland in College Park, United States. His research interests include big-data analytics for cybersecurity, cyber-physical system risk assessment, cybercrime markets, and analysis of large-scale networks.</style></custom3><custom4><style face="normal" font="default" size="100%">Carleton University
Michael Weiss holds a faculty appointment in the Department of Systems and Computer Engineering at Carleton University in Ottawa, Canada, and he is a member of the Technology Innovation Management program. His research interests include open source, ecosystems, mashups, patterns, and social network analysis. Michael has published on the evolution of open source business, mashups, platforms, and technology entrepreneurship.</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tony Bailetti</style></author><author><style face="normal" font="default" size="100%">Mahmoud Gad</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Examining the Modes Malware Suppliers Use to Provide Goods and Services</style></title><secondary-title><style face="normal" font="default" size="100%">Technology Innovation Management Review</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">agents</style></keyword><keyword><style  face="normal" font="default" size="100%">customers</style></keyword><keyword><style  face="normal" font="default" size="100%">cybercrime</style></keyword><keyword><style  face="normal" font="default" size="100%">cybersecurity</style></keyword><keyword><style  face="normal" font="default" size="100%">malware</style></keyword><keyword><style  face="normal" font="default" size="100%">modes</style></keyword><keyword><style  face="normal" font="default" size="100%">multisided platform</style></keyword><keyword><style  face="normal" font="default" size="100%">suppliers</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">02/2016</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://timreview.ca/article/965</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Talent First Network</style></publisher><pub-location><style face="normal" font="default" size="100%">Ottawa</style></pub-location><volume><style face="normal" font="default" size="100%">6</style></volume><pages><style face="normal" font="default" size="100%">21-27</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Malware suppliers use various modes to provide goods and services to customers. By mode, we mean “the way” the malware supplier chooses to function. These modes increase monetization opportunities and enable many security breaches worldwide. A theoretically sound framework that can be used to examine the various modes that malware suppliers use to produce and sell malware is needed. We apply a general model specified recently by Hagiu and Wright to study five modes that malware suppliers use to deliver goods and services to their customers. The framework presented in this article can be used to predict the mode in which a malware supplier will function; to study which types of malware suppliers, agents, and customers are attracted to each mode; to discover new modes; and to better understand the threat a malware supplier presents.</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><custom1><style face="normal" font="default" size="100%">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.</style></custom1><custom2><style face="normal" font="default" size="100%">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 and 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.</style></custom2></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tony Bailetti</style></author><author><style face="normal" font="default" size="100%">Mahmoud Gad</style></author><author><style face="normal" font="default" size="100%">Ahmed Shah</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Intrusion Learning: An Overview of an Emergent Discipline</style></title><secondary-title><style face="normal" font="default" size="100%">Technology Innovation Management Review</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">adversarial learning</style></keyword><keyword><style  face="normal" font="default" size="100%">clustering</style></keyword><keyword><style  face="normal" font="default" size="100%">cybersecurity</style></keyword><keyword><style  face="normal" font="default" size="100%">enterprise</style></keyword><keyword><style  face="normal" font="default" size="100%">intrusion detection</style></keyword><keyword><style  face="normal" font="default" size="100%">intrusion learning</style></keyword><keyword><style  face="normal" font="default" size="100%">learning algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">machine learning</style></keyword><keyword><style  face="normal" font="default" size="100%">real-time analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">resiliency</style></keyword><keyword><style  face="normal" font="default" size="100%">security</style></keyword><keyword><style  face="normal" font="default" size="100%">streaming network data</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">02/2016</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://timreview.ca/article/964</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Talent First Network</style></publisher><pub-location><style face="normal" font="default" size="100%">Ottawa</style></pub-location><volume><style face="normal" font="default" size="100%">6</style></volume><pages><style face="normal" font="default" size="100%">15-20</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><custom1><style face="normal" font="default" size="100%">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.</style></custom1><custom2><style face="normal" font="default" size="100%">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.</style></custom2><custom3><style face="normal" font="default" size="100%">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. </style></custom3></record></records></xml>