Machine Learning for Networking

It is an exciting time to work in networking and networked applications. This talk will examine how machine learning (ML) benefits networking by focusing on four examples. First, we’ll examine Intent-Based Networking (a modern architecture for designing and operating a network) and how ML can be used to increase visibility, diagnose problems, identify associated remedies, and provide assurance that the network is operating as intended. Next, we’ll examine how to understand what devices are on the network, which is a key step to providing customized network performance and protecting those devices. In the context of ever-growing security threats, we’ll examine how ML can be applied to address the challenge of malware sneaking in an encrypted flow. Specifically, how can we detect malware hidden in encrypted flows without requiring decryption of those flows? Lastly, we’ll look at how the move from today’s Cloud-based ML to the promising approach of Distributed ML across Edge and Cloud can lead to improved scalability, reduced latency, and improved privacy. It is noteworthy that while ML is often associated with reducing privacy, the last two examples showcase how an elegant application of ML can achieve the desired goal while preserving privacy.