Transformer-based Compound Correlation Miner for Smart Home Anomaly Detection

Andrew D'Angelo, Chenglong Fu, Xiaojiang Du, Paul Ratazzi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

IoT-enabled smart homes can be double-edged swords. On one side, the convenience and efficiency brought about by smart device integrations can improve the standard of living dramatically. However, on the other side, the prevalent interconnectivity among home devices can drastically increase the potential risk of attack. Therefore, in order to reduce the possibility of a successful attack we propose a complex correlation-based anomaly detection system powered by intricate two-to-one correlations. Through the use of a state-of-the-art transformer model, we present a novel correlation mining mechanism that leverages the power of attention weights to develop an understanding of the underlying correlations that exist between IoT events in a smart home environment. Using this knowledge, we use a special validation algorithm to verify 52 two-to-one correlations in our system. Furthermore, we simulate four distinct attack scenarios and attain an average detection accuracy and recall of 96.59% and 97.38% respectively. Our results indicate that our method is effective at identifying a range of IoT attacks and successfully demonstrates the capabilities of IoT correlations.

Original languageEnglish
Title of host publication2023 IEEE 12th International Conference on Cloud Networking, CloudNet 2023
Pages281-289
Number of pages9
ISBN (Electronic)9798350313062
DOIs
StatePublished - 2023
Event12th IEEE International Conference on Cloud Networking, CloudNet 2023 - Hoboken, United States
Duration: 1 Nov 20233 Nov 2023

Publication series

Name2023 IEEE 12th International Conference on Cloud Networking, CloudNet 2023

Conference

Conference12th IEEE International Conference on Cloud Networking, CloudNet 2023
Country/TerritoryUnited States
CityHoboken
Period1/11/233/11/23

Keywords

  • Anomaly Detection
  • Security
  • Smart home

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