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WiP: The Intrinsic Dimensionality of IoT Networks

Published: 08 June 2022 Publication History

Abstract

The Internet of Things (IoT) is revolutionizing society by connecting people and devices seamlessly and providing enhanced user experience and functionalities. However, the unique properties of IoT networks, such as heterogeneity and non-standardized protocol, have created critical security holes and network mismanagement. We propose a new measurement tool for IoT network data to aid in analyzing and classifying such network traffic. We use evidence from both security and machine learning research, which suggests that the complexity of a dataset can be used as a metric to determine the trustworthiness of data. We test the complexity of IoT networks using Intrinsic Dimensionality (ID), a theoretical complexity measurement based on the observation that a few variables can often describe high dimensional datasets. We use ID to evaluate four modern IoT network datasets empirically, showing that, for network and device-level data generated using IoT methodologies, the ID of the data fits into a low dimensional representation; this makes such data amenable to the use of machine learning algorithms for anomaly detection.

Supplementary Material

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I review the need for a formalized complexity measurement in security, and show how intrinsic dimensionality can be used. I then review how IoT networks exhibit low intrinsic dimensionality

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cover image ACM Conferences
SACMAT '22: Proceedings of the 27th ACM on Symposium on Access Control Models and Technologies
June 2022
282 pages
ISBN:9781450393577
DOI:10.1145/3532105
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Published: 08 June 2022

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Author Tags

  1. IoT
  2. data complexity
  3. internet of things
  4. intrinsic dimensionality
  5. intrusion detection

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  • (2024)Refined Intrusion Detection Model for Internet of Things Networks2024 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES)10.1109/SPICES62143.2024.10779933(1-6)Online publication date: 20-Sep-2024
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  • (2024)A Generalized Lightweight Intrusion Detection Model With Unified Feature Selection for Internet of Things NetworksInternational Journal of Network Management10.1002/nem.2291Online publication date: 23-Jul-2024
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  • (2023)Comparative Study on Different Intrusion Detection Datasets Using Machine Learning and Deep Learning AlgorithmsBig Data and Cloud Computing10.1007/978-981-99-1051-9_8(109-120)Online publication date: 11-Jun-2023
  • (2022)Local Intrinsic Dimensionality of IoT Networks for Unsupervised Intrusion DetectionData and Applications Security and Privacy XXXVI10.1007/978-3-031-10684-2_9(143-161)Online publication date: 18-Jul-2022

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