Abstract
Information technology and telecommunication is considered a new and quickly evolving branch of science. New technologies and services in IT and telecommunications impose successive changes and updates on related engineering majors, especially in practical qualification that includes using software facilities. This chapter aims to join the efforts to spread the use of open source software in academic education. The chapter consists of two main sections. The first presents the trend of using open source software in higher education and discusses pros and cons of using open source software in engineering education. The second section presents network traffic analysis as an example of recent effective research topics and provides a set of open source tools to perform the research's practical steps. The research example with the suggested tools is valid as practical lab work for telecommunication and IT-related majors.
TopOpen Source Software In Education And Research
Tendency to Use Open Source Software in Higher Education
By definition, open source software (OSS) is software that is available to everyone, including the source code, along with the copyright license that permits using, studying, modifying, or redistributing the software (Beal, 2008). OSS covers a wide range of user needs, ranging from simple programs such as editing utilities, to very advanced software such as operating systems. The most famous successes in OSS are the operating systems Linux and Android.
Using OSS in education is a current tendency in some of the leading universities around the world, including the USA and Europe (Wilson, 2013; Roach, 2016). Some of these universities, such as MIT and Stanford, effectively participate in developing open source projects through their dedicated research labs. The main reasons for choosing OSS in many educational institutions are the cost, which plays a key role especially in limited-budget educational systems; its high effectiveness and success with some important educational platform systems such as Moodle (Cole & Foster, 2008); and better suitability than closed software for research environments in higher education. Nowadays, there is a tendency in some countries to share information and make education available to everyone (e.g., the #GoOpen campaign in the USA) (Office of Educational Technology, 2016). Open source and open education complement each other, and both focus on transparency and sharing information. The next section presents pros and cons of using OSS in engineering education.
Key Terms in this Chapter
Machine Learning Tools: A software which implements learning algorithms to resolve prediction problems.
Network Traffic Identification: A kind of network traffic classification with interests in one or more specific applications.
Sniffer: A kind of software is used to capture network traffic.
Flow Features Extracting: The process of getting specific flow metrics from network traces, these metrics are used to identify the application (or application type) which generated the flow.
Engineering Preparation: The process of teaching and qualifying the students with the basic knowledge and skills in specific engineering domain.
Free Software: A kind of software that available for everyone without any constraints on usage, modification or redistribution.
Laboratory’s Software: The used software in laboratory to teach students.
Open Source Software (OSS): A free computer program, available with its source code for everyone to use, modify, and redistribute to the others under some terms of usage.
Data Mining: A computer process that discovers hidden information or relations in a large amount of data, using artificial intelligence and machine learning methods.
Commercial Software: Any developed software for commercial purposes and that has a price and a license.
Network Traffic Labeling: A process which maps the network flows with the convenient application or application type.
Application Tunneling: A method of encapsulating the traffic of a prohibited application in the payload of a legal application.
Network Traffic Analysis: A process of network logs manipulation in order to know the used applications, protocol addresses, or to get statistics of network usage.
Deep Learning: A recent method of machine learning based on neural networks with more than one hidden layer.