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Open Access Thesis
Electrical & Computer Engineering
Master of Science in Electrical and Computer Engineering (M.S.E.C.E.)
Year Degree Awarded
Month Degree Awarded
Time and again we have seen the Internet grow and evolve at an unprecedented scale. The number of online users in 1995 was 40 million but in 2020, number of online devices are predicted to reach 50 billion, which would be 7 times the human population on earth. Up until now, the revolution was in the digital world. But now, the revolution is happening in the physical world that we live in; IoT devices are employed in all sorts of environments like domestic houses, hospitals, industrial spaces, nuclear plants etc., Since they are employed in a lot of mission-critical or even life-critical environments, their security and reliability are of paramount importance because compromising them can lead to grave consequences.
IoT devices are, by nature, different from conventional Internet connected devices like laptops, smart phones etc., They have small memory, limited storage, low processing power etc., They also operate with little to no human intervention. Hence it becomes very important to understand IoT devices better. How do they behave in a network? How different are they from traditional Internet connected devices? Can they be identified from their network traffic? Is it possible for anyone to identify them just by looking at the network data that leaks outside the network, without even joining the network? That is the aim of this thesis. To the best of our knowledge, no study has collected data from outside the network, without joining the network, with the intention of finding out if IoT devices can be identified from this data. We also identify parameters that classify IoT and non-IoT devices. Then we do manual grouping of similar devices and then do the grouping automatically, using clustering algorithms. This will help in grouping devices of similar nature and create a profile for each kind of device.
Anbazhagan, Mythili Vishalini, "An Empirical Analysis of Network Traffic: Device Profiling and Classification" (2019). Masters Theses. 756.