Landing Reinforcement Learning onto Smart Scanning of The Internet of Things

Jian Qu, Xiaobo Ma, Wenmao Liu, Hongqing Sang, Jianfeng Li, Lei Xue, Xiapu Luo, Zhenhua Li, LI Feng, Xiaohong Guan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

2 Citations (Scopus)

Abstract

Cyber search engines, such as Shodan and Censys, have gained popularity due to their strong capability of indexing the Internet of Things (IoT). They actively scan and fingerprint IoT devices for unearthing IP-device mapping. Because of the large address space of the Internet and the mapping’s mutative nature, efficiently tracking the evolution of IP-device mapping with a limited budget of scans is essential for building timely cyber search engines. An intuitive solution is to use reinforcement learning to schedule more scans to networks with high churn rates of IP-device mapping. However, such an intuitive solution has never been systematically studied. In this paper, we take the first step toward demystifying this problem based on our experiences in maintaining a global IoT scanning platform. Inspired by the measurement study of large-scale real-world IoT scan records, we land reinforcement learning onto a system capable of smartly scanning IoT devices in a principled way. We disclose key parameters affecting the effectiveness of different scanning strategies, and find that our system would achieve growing advantages with the proliferation of IoT devices.
Original languageEnglish
Title of host publicationINFOCOM 2022 - IEEE Conference on Computer Communications
PublisherIEEE
Pages2088-2097
Number of pages10
ISBN (Electronic)978-1-6654-5822-1
DOIs
Publication statusPublished - 20 Jun 2022

Publication series

NameProceedings - IEEE INFOCOM
Volume2022-May
ISSN (Print)0743-166X

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