Automatic Test Framework Anomaly Detection in Home Routers

In a modern world most people have a home network and multiple devices behind it. These devices include simple IoT, that require external protection not to join a botnet. This protection can be granted by a security router with a feature of determining the usual network traffic of a device and alerting its unusual behaviour. This work is dedicated to creating a testbed to verify such router's work. The test bed includes tools to capture IoT traffic, edit and replay it. Created tool supports UDP, TCP, partially ICMP and is extendable to other protocols. UDP and TCP protocols are replayed using OS sockets at transport network layer. The methods described have proved to work on a real setup

GumDrop at the DISRPT2019 Shared Task: A Model Stacking Approach to Discourse Unit Segmentation and Connective Detection

In this paper we present GumDrop, Georgetown University's entry at the DISRPT 2019 Shared Task on automatic discourse unit segmentation and connective detection. Our approach relies on model stacking, creating a heterogeneous ensemble of classifiers, which feed into a metalearner for each final task. The system encompasses three trainable component stacks: one for sentence splitting, one for discourse unit segmentation and one for connective detection. The flexibility of each ensemble allows the system to generalize well to datasets of different sizes and with varying levels of homogeneity.Comment: Proceedings of Discourse Relation Parsing and Treebanking (DISRPT2019