Network Security Research Group

Network Security Research Group

About

Network security is a very important factor in today's information-driven world. According to Internet World Stats and miniwatts marketing group as of June 2014, there were more than 3 billion online users throughout the world and this number keeps growing. With the number growing, the potential risks for corporate and individual users are also growing. We now use the internet for more than just information gathering. According to eMarketer global online retail sales have topped $1trillion and will continue to grow $1,321.4 billion by 2017 ("Trends & Data - Internet Retailer," n.d.). A market this size will require extra precautions from Network Attacks and Intrusions.

Network Security Research group focuses on using machine learning techniques on Network Intrusion-detection systems as well as Mobile Phone security and biometric security. Specialized Machine techniques are Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Decision Tree methods.

Background and Motivation

The "Network Security" Research group is a part of the Faculty of Information Technologies and Engineering at the International Burch University. The group member structure changed over the time. The current members of the group are three professors, three Ph.D. candidates, and two Master students. The group focuses on Network Security, Intrusion-detection, Biometric Security, Mobile Security using Machine Learning and Data Mining techniques.

Network Security research at the International Burch University was initiated in 2009 when prof. Abdulhamit Subasi (former head of the group) came to the University and started to work as Dean of Faculty. During this period, members of the group did a number of scientific researches. With a wide range of researches being a member of the group and close cooperation with IT Center (University IT office in charge of University network and computers), Network security group has the unique advantage of gathering production data from university networks for research and test purposes.