Research Projects

Cybersecurity threats are on the rise, with more sophisticated and difficult-to-detect cyberattacks. To address this, the focus is on developing efficient and cost-effective cybersecurity solutions for on-premise, Datacenter, Enterprise, and Multi-access edge computing (MEC) networks. The proposed work aims to develop real-time high-precision distributed traffic monitoring, a high-performance machine learning and inference platform, and specific algorithms for real-time analytics of large-scale data collected in real-time. The primary goal of this project is to design and implement a distributed solution for network intelligence to enable real-time network data analytics towards realizing a self-healing and self-organizing network. This involves designing a distributed real-time monitoring system and building protocols that cater for timely monitoring and processing of large-scale data. The project aims to design and implement a high-performance distributed monitoring system that can handle large amounts of online streaming data and develop novel data analytic techniques that combine both black-box and white-box based machine learning techniques. The project envisions providing a distributed framework and a working prototype of a network intelligence platform that can cater to the growing needs of both mobile and datacenter networks, serving as a timely and fine-grained traffic analytic platform. In conclusion, the goal of this project is to develop a distributed network intelligence platform that can cater to the growing needs of mobile and datacenter networks, providing efficient and cost-effective cybersecurity solutions.