Side Channel Attacks on Identity Privacy
Side-channel attacks are a class of security exploits that gather information indirectly from a system. This project explores how these attacks could compromise identity privacy in network traffic. Specifically, it aims to classify traffic flows generated by a user without relying only on source and destination IP addresses, but instead using inter-packet arrival time (IPAT) and packet sizes.
As side-channel attacks become more common, concerns over online communication privacy and security are growing. Even if users take measures such as using VPNs or proxy servers, sensitive information may still be revealed. This work aims to address these concerns.
Keywords
Traffic Analysis, Computer Security, Machine Learning
Staff
[Saleem Bhatti]{snb6}