Abstract:
It is an issue of both security and management for all network administrators
to determine the Operating Systems (OS) that are using their network. Identification
of Operating Systems in any kind of network has been a real challenge due to the rapid
changes of the encryption protocols and the quick enlargement of the data.
In order to solve this problem, there are plenty active and passive
fingerprinting methods than can lead to finding the real OS behind the traffic, but on
top of these outdated methods, the one that has a great interest from all researchers is
undoubtfully using Machine Learning (ML). The difficulties in this field starts from
building the dataset, to choosing the best algorithm to find the OS from some simple
features of TCP/IP packets or from TLS handshake information.
In this thesis we will show how can OS fingerprinting can be achieved with
machine learning and what are the tools that one may need to do this task. We will
state also different methods of OS fingerprinting using network traffic.
