Date of Award
6-14-2024
Document Type
Thesis
Publisher
Santa Clara : Santa Clara University, 2024
Department
Computer Science and Engineering
First Advisor
Behnam Dezfouli
Abstract
As internet security concerns grow due to the increasing complexity of cyberattacks, heterogeneity of network-connected devices, and popularity of insecure Internet of Things (IoT) devices, network administrators require more intelligent ways of managing and securing their networks. One crucial step in this regard is the ability to identify and classify devices from the edge of networks. However, many recent advances in network security come at the cost of user privacy, as device classification algorithms often require packet payload data, Internet Protocol (IP) addresses, or other sensitive information. This paper proposes the Shenassa-Castillo Device Identifier (SCDI): a software network device identifier that does not use any potentially sensitive features in classification. The novel feature used for device classification is Acknowledgment Response Time (ART)—that is, the time between a Transmission Control Protocol (TCP) packet and its related acknowledgment. We examine the efficacy ART in distinguishing between heterogeneous devices and its shortcomings in distinguishing between homogeneous devices. We train a Random Forest classifier on a dataset of seven devices and achieve accuracy and F1 scores of 88.3% and 87.2%, respectively.
Recommended Citation
Shenassa, Ethan and Castillo, Michael, "SCDI: Privacy-Preserving Device Identification" (2024). Computer Science and Engineering Senior Theses. 297.
https://scholarcommons.scu.edu/cseng_senior/297