BGP prefix hijacking is a critical threat to the resilience and security of communications in the Internet. While several mechanisms have been proposed to prevent, detect or mitigate hijacking events, it has not been studied how to accurately quantify the impact of an ongoing hijack. When detecting a hijack, existing methods do not estimate how many networks in the Internet are affected (before and/or after its mitigation). In this paper, we study fundamental and practical aspects of the problem of estimating the impact of an ongoing hijack through network measurements. We derive analytical results for the involved trade-offs and limits, and investigate the performance of different measurement approaches (control/data-plane measurements) and use of public measurement infrastructure. Our findings provide useful insights for the design of accurate hijack impact estimation methodologies. Based on these insights, we design (i) a lightweight and practical estimation methodology that employs ping measurements, and (ii) an estimator that employs public infrastructure measurements and eliminates correlations between them to improve the accuracy. We validate the proposed methodologies and findings against results from hijacking experiments we conduct in the real Internet.

Modern IoT solutions require minimisation of the transmitted data during a firmware update, in order to save energy for the constrained devices. To accommodate this requirement, IoT nodes can be updated incrementally using only parts of the current firmware, which is already stored in their flash memory in order to reconstruct the new firmware locally. This the role of the so-called differencing algorithm that executes in a firmware server and aims to detect common segments between the current and the new firmware, producing an encoded small delta script, which is finally transmitted to the IoT nodes. In this work, we present a differencing algorithm that operates in byte-level and can compute optimal, in terms of size, delta scripts in O(nlogn) time and O(n) space complexity.