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MS Final Exam – Jiaqi Bao

Network Traffic-Driven Hybrid Learning for Classifying Seen and Unseen IoT Device Types

IoT networks can be viewed as collections of Internet-enabled physical devices and objects, embedded with sensor, actuator, computation, storage and communication components, that are capable of connecting and exchanging data to one another. In recent years, organizations have allowed more and more IoT devices to be connected to their networks, thereby increasing their risks of and exposure to security vulnerabilities and threats. Therefore, it is important for such organizations to be able to identify which devices are connected to their network and which ones are legitimate and pose no risk. Leveraging network traffic to identify devices through supervised learning has recently been gaining popularity, where feature information is first extracted by intercepting device traffic and then exploited to provide device classification. The main limitation of prior works is that they can only identify previously seen types of devices, and any newly added device ty! pes are treated as abnormal types. In the real world, hundreds of millions of new IoT devices are produced each year, and the lack of a large amount of training data makes a system based solely on supervised learning unrealistic. In this paper, we propose a hybrid supervised and unsupervised learning method that enables secondary classification of unseen device types. Our technique combines deep neural networks with clustering to enable both seen and unseen device classification, and employs autoencoder technique to reduce dimensionality of datasets, thereby providing a good balance between classification accuracy and overhead.

Major Advisor: Bechir Hamdaoui
Committee: Huaping Liu
Committee: Weng-Keen Wong
GCR: Yun-Shik Lee

Tuesday, March 17 at 3:00pm to 5:00pm

Kelley Engineering Center, 1007
110 SW Park Terrace, Corvallis, OR 97331

Event Type

Lecture or Presentation

Event Topic

Research

Organization
Electrical Engineering and Computer Science
Contact Name

Dakota Nelson

Contact Email

eecs.gradinfo@oregonstate.edu

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