02583nas a2200241   4500008004100000022001400041245010600055210006900161260004200230300001000272490000600282520183100288653002202119653001802141653002502159653002702184653001802211100001602229700002302245700001702268700001902285856003702304       2017                        eng d  a1927-032100aCombining Exploratory Analysis and Automated Analysis for Anomaly Detection in Real-Time Data Streams0 aCombining Exploratory Analysis and Automated Analysis for Anomal  aOttawabTalent First Networkc04/2017  a25-310 v73 aSecurity 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.10aanomaly detection10acybersecurity10aexploratory analysis10areal-time data streams10avisualization1 aShah, Ahmed1 aAbualhaol, Ibrahim1 aGad, Mahmoud1 aWeiss, Michael  uhttp://timreview.ca/article/106801883nas 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/96402511nas a2200253   4500008004100000022001400041245006100055210006100116260004200177300001000219490000600229520182100235653001802056653001402074653001802088653000802106653001202114653001602126653002102142100001602163700001902179700002302198856003602221       2016                        eng d  a1927-032100aLicense Compliance in Open Source Cybersecurity Projects0 aLicense Compliance in Open Source Cybersecurity Projects  aOttawabTalent First Networkc02/2016  a28-350 v63 aDevelopers of cybersecurity software often include and rely upon open source software packages in their commercial software products. Before open source code is absorbed into a proprietary product, developers must check the package license to see if the project is permissively licensed, thereby allowing for commercial-friendly inheritance and redistribution. However, there is a risk that the open source package license could be inaccurate due to being silently contaminated with restrictively licensed open source code that may prohibit the sale or confidentiality of commercial derivative work. Contamination of commercial products could lead to expensive remediation costs, damage to the company's reputation, and costly legal fees. In this article, we report on our preliminary analysis of more than 200 open source cybersecurity projects to identify the most frequently used license types and languages and to look for evidence of permissively licensed open source projects that are likely contaminated by restrictive licensed material (i.e., containing commercial-unfriendly code). Our analysis identified restrictive license contamination cases occurring in permissively licensed open source projects. Furthermore, we found a high proportion of code that lacked copyright attribution. We expect that the results of this study will: i) provide managers and developers with an understanding of how contamination can occur, ii) provide open source communities with an understanding on how they can better protect their intellectual property by including licenses and copyright information in their code, and ii) provide entrepreneurs with an understanding of the open source cybersecurity domain in terms of licensing and contamination and how they affect decisions about cybersecurity software architectures.10acontamination10acopyright10acybersecurity10aGPL10alicense10aopen source10athird-party code1 aShah, Ahmed1 aSelman, Selman1 aAbualhaol, Ibrahim  uhttp://timreview.ca/article/966