TY - GEN
T1 - Transformer-based Compound Correlation Miner for Smart Home Anomaly Detection
AU - D'Angelo, Andrew
AU - Fu, Chenglong
AU - Du, Xiaojiang
AU - Ratazzi, Paul
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Anomaly Detection
KW - Security
KW - Smart home
UR - http://www.scopus.com/inward/record.url?scp=85191239806&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85191239806&partnerID=8YFLogxK
U2 - 10.1109/CloudNet59005.2023.10490063
DO - 10.1109/CloudNet59005.2023.10490063
M3 - Conference contribution
AN - SCOPUS:85191239806
T3 - 2023 IEEE 12th International Conference on Cloud Networking, CloudNet 2023
SP - 281
EP - 289
BT - 2023 IEEE 12th International Conference on Cloud Networking, CloudNet 2023
T2 - 12th IEEE International Conference on Cloud Networking, CloudNet 2023
Y2 - 1 November 2023 through 3 November 2023
ER -