Publications

Machine learning techniques are a common solution used to solve a variety of network management tasks. Often, a network administrator chooses the model to deploy based on offline information, such as model performance and system load. Yet, network traffic is inherently dynamic making it hard to select an optimal model that can work throughout ever changing conditions. In this paper, we make the case that, instead of having to select the optimal candidate model based on offline information, systems should adapt based on the network conditions observed. We present a system design that takes as input a set of candidate models and their features, and adaptively select the better configuration as a function of the network and the system conditions.

MAC addresses can be collected by passive observers to track Wi-Fi users. While address randomization neutralizes this threat for devices not yet associated, the problem remains for devices being associated with a WLAN. In this paper, we introduce RoMA, which is an anti-tracking scheme making use of concurrent virtual network interfaces (VIFs). We provide a proof-of-concept implementation of RoMA and show that it has a limited impact on the performance of the devices.