IoT Security: Directing Network Traffic
This course was developed by IEEE Educational Activities with the support from IEEE Internet of Things Technical Community. In this course, we will first examine a network configuration that augments a local area network connected to the Internet so that all WAN and LAN traffic is recorded to an out-of-band server. Next, we will review machine learning as an approach applicable to solving various problems in many research domains. We will examine IoT testbeds and datasets containing network traffic generated by IoT devices. We will consider techniques for configuring smart home IoT testbed for the traffic collection purposes. We will learn to use program tools for collecting network traffic, manipulate collected data, and extract network traffic features. Finally, we will examine how to preprocess collected data and finally use the collected dataset in solving cybersecurity challenges raised by IoT devices.
What you will learn:
- Implement a complete network traffic logging system for situations where logging must go undetected
- Understand the need for IoT testbed and IoT network traffic datasets
- Configure smart home IoT testbed for collection of generated network traffic
- Use program tools for collecting network traffic, manipulate collected data, and extract network traffic features
- Preprocess collected data
- Use collected dataset in solving cybersecurity challenges raised by IoT devices
This course is part of the following course program:
All About IoT Security
Courses included in this program:
Who should attend: Electrical Engineer, Design Engineer, Communications Systems Engineer, Product Engineer, Computer Engineer, Software Engineer, Project Engineer, Software/Security Engineer, AI/ML Engineer
Dr. Slavin is an Assistant Professor in the Department of Computer Science at the University of Texas at San Antonio. He received his PhD degree in Computer Science at the University of Texas at San Antonio in 2017. Dr. Slavin’s lab incorporates multi-disciplinary approaches toward practical privacy risk mitigation in mobile and IoT settings. The approach described in this tutorial was developed and used for multiple funded projects.
Ivan Cvitić, Ph.D. is postdoctoral researcher and scientific associate at University of Zagreb, Faculty of Transport and Traffic Sciences. actively researching several problem areas. Primary research domain is cybersecurity with focus on securing the availability of information and communication resources. He is actively engaged in scientific and research work as an associate in the Laboratory for Security and Forensic Analysis of Information and Communication System in the field of information and communication traffic focusing on cybersecurity research, IoT concept, and applied machine learning. He received his PhD (summa cum laude) in 2020 by defending his PhD thesis titled Network traffic anomaly detection based on traffic characteristics and device class affiliation. As a result of research activities, he published more then 50 scientific papers in scientific journals indexed in WoS (Web of Science), and Scopus bases, and proceedings of international scientific conferences and scientific books. He is technical program committee chair or member for several EAI (European Association for Innovations) conferences. He is editorial bord member, guest editor and reviewer for various respected journals. He was appointed as a member of the Thematic Innovation Council for Security by the Government of the Republic of Croatia and the Innovation Council for Industry of the Republic of Croatia.
Kim-Kwang Raymond Choo
Kim-Kwang Raymond Choo received a Ph.D. in information security in 2006 from Queensland University of Technology, Australia. He currently holds the Cloud Technology Endowed Professorship at The University of Texas at San Antonio. He is the founding co-editor-in-chief of ACM Distributed Ledger Technologies: Research and Practice, and the founding Chair of IEEE Technology and Engineering Management Society Technical Committee (TC) on Blockchain and Distributed Ledger Technologies. He is the recipient of the 2022 IEEE Hyper-Intelligence TC Award for Excellence in Hyper-Intelligence Systems (Technical Achievement Award), the 2022 IEEE TC on Homeland Security Research and Innovation Award, the 2022 IEEE TC on Secure and Dependable Measurement Mid-Career Award, and the 2019 IEEE TC on Scalable Computing Award for Excellence in Scalable Computing (Middle Career Researcher).
Publication Year: 2023