A Survey on User Profiling Model for Anomaly Detection in Cyberspace

Authors

  • Arash Habibi Lashkari Canadian Institute for Cybersecurity (CIC), University of New Brunswick (UNB) Fredericton, Canada
  • Min Chen Canadian Institute for Cybersecurity (CIC), University of New Brunswick (UNB) Fredericton, Canada
  • Ali A. Ghorbani Canadian Institute for Cybersecurity (CIC), University of New Brunswick (UNB) Fredericton, Canada

DOI:

https://doi.org/10.13052/2245-1439.814

Keywords:

User Profiling, Cybersecurity Profiling, Big Security Data, Security Data Source, Security Profiling Features, Anomaly Detection, Cybersecurity forewarning system

Abstract

In the face of escalating global Cybersecurity threats, having an automated forewarning system that can find suspicious user profiles is paramount. It can work as a prevention technique for planned attacks or ultimate security breaches. Significant research has been established in attack prevention and detection, but has demonstrated only one or a few different sources with a short list of features. The main goals of this paper are, first, to review the previous user profiling models and analyze them to find their advantages and disadvantages; second, to provide a comprehensive overview of previous research to gather available features and data sources for user profiling; third, based on the deficiencies of the previous models, the paper proposes a new user profiling model that can cover all available sources and related features based on the cybersecurity perspective. The proposed model includes seven profiling criteria for gathering user’s information and more than 270 features to parse and generate the security profile of a user.

 

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Author Biographies

Arash Habibi Lashkari, Canadian Institute for Cybersecurity (CIC), University of New Brunswick (UNB) Fredericton, Canada

Arash Habibi Lashkari is an assistant professor at the Faculty of Computer Science, University of New Brunswick (UNB) and research manager of the Canadian Institute for Cybersecurity (CIC). He has more than 22 years of academic and industry experience developing technology that detects and protects against cyberattacks, malware and the dark web. Dr. Lashkari has been awarded 3 gold medals as well as 12 silver and bronze medals in international computer security competitions around the world. In 2017, he has been selected as the top 150 researchers who will shape the future of Canada. Also, he won the Runner up Cybersecurity Academic Award of the year at ICSIC conference in Canada. He is the author of 10 books in English and Persian on topics including cryptography, network security, and mobile communication as well as over 80 journals and conference papers concerning various aspects of computer security. His research focuses on cybersecurity, big data security analysis, Internet traffic analysis and the detection of malware and cyber-attacks as well as generating cybersecurity datasets.

Min Chen, Canadian Institute for Cybersecurity (CIC), University of New Brunswick (UNB) Fredericton, Canada

Min Chen is a postdoctoral fellow at Canadian Institute for Cybersecurity (CIC) on the Faculty of Computer Science, University of New Brunswick. She has extensive academic experience in the areas of machine learning, service computing and cybersecurity. She has several conference and journal publications in the research area of machine learning and service computing. Currently, she is interested in studying user profiling in the respective of cybersecurity with machine learning technology. Her research focused on modeling user behavior as a prevention technique for planned attacks.

Ali A. Ghorbani, Canadian Institute for Cybersecurity (CIC), University of New Brunswick (UNB) Fredericton, Canada

Ali A. Ghorbani has held a variety of positions in academia for the past 35 years and is currently the Canada Research Chair (Tier 1) in Cybersecurity, the Dean of the Faculty of Computer Science (since 2008), and the Director of the Canadian Institute for Cybersecurity. He is the co-inventor on 3 awarded patents in the area of Network Security and Web Intelligence and has published over 200 peer-reviewed articles during his career. He has supervised over 160 research associates, postdoctoral fellows, graduate and undergraduate students during his career. His book, Intrusion Detection and Prevention Systems: Concepts and Techniques, was published by Springer in October 2010. In 2007, Dr. Ghorbani received the University of New Brunswick’s Research Scholar Award. Since 2010, he has obtained more than $10M to fund 6 large multi-project research initiatives. Dr. Ghorbani has developed a number of technologies that have been adopted by high-tech companies. He co-founded two startups, Sentrant and EyesOver in 2013 and 2015. Dr. Ghorbani is the co-Editor-In-Chief of Computational Intelligence Journal. He was twice one of the three finalists for the Special Recognition Award at the 2013 and 2016 New Brunswick KIRA award for the knowledge industry.

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Published

2018-09-22

How to Cite

1.
Lashkari AH, Chen M, Ghorbani AA. A Survey on User Profiling Model for Anomaly Detection in Cyberspace. JCSANDM [Internet]. 2018 Sep. 22 [cited 2024 Nov. 5];8(1):75-112. Available from: https://journals.riverpublishers.com/index.php/JCSANDM/article/view/5321

Issue

2019: Vol 8 Iss 1

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Articles