UC Riverside

Machine learning has opened up numerous opportunities and applications for vari-ous networking applications. This dissertation focuses on ML-based solutions in constrained environments for different networking applications.

First, we present a framework that detects and summarizes key global eventsfrom distributed crowd-sensed data in a bandwidth-constrained environment. We introduce BigEye, a novel framework that only transfers limited data from distributed producers to a central summarizer, supporting highly accurate detection and concise visual summarization of key events of global interest.

Second, we develop AcTrak, a framework to control steerable cameras through anetwork to retrieve telemetrics of interest. AcTrak automates a camera’s motion to switch appropriately between zooming in on existing targets in a scene to track their activities and zooming out to search for new targets arriving in the area of interest. We aim to achieve a good trade-off between these two tasks, ensuring that new targets are observed by the camera before they leave the scene while frequently monitoring the activities of existing targets.

Third, we uncover a vulnerability that enables fast and stealthy data exfiltrationover DNS channels. While existing defenses against such attacks appear robust, we demon- strate that our carefully designed and novel DNS exfiltration attack, Dolos, which uses a generative adversarial network (GAN), can encode sensitive data to evade these detec- tors unlike existing state-of-the-art attack methods. Additionally, Dolos can significantly expedite exfiltration compared to prior methods.