TY - GEN
T1 - A Hybrid Convolutional Neural Networks and Logistic Regression Framework for Robust Cyber Attack Detection in Artificial Intelligence of Things (AIoT)
AU - Gupta, Brij B.
AU - Gaurav, Akshat
AU - Arya, Varsha
AU - Chui, Kwok Tai
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - In the current environment of the Artificial Intelligence of Things(AIoT), the necessity to develop efficient cyber attack detection systems is essential. In this regard, this paper introduces a hybrid framework which takes advantage of the feature extraction capabilities of Convolutional Neural Networks and the prediction abilities of Logistic Regression. Throughout analysis, our model has shown an accuracy of 92%, while both precision and F1-scores have reached 0.94 and 0.93, respectively.
AB - In the current environment of the Artificial Intelligence of Things(AIoT), the necessity to develop efficient cyber attack detection systems is essential. In this regard, this paper introduces a hybrid framework which takes advantage of the feature extraction capabilities of Convolutional Neural Networks and the prediction abilities of Logistic Regression. Throughout analysis, our model has shown an accuracy of 92%, while both precision and F1-scores have reached 0.94 and 0.93, respectively.
KW - Artificial Intelligence of Things (AIoT)
KW - Convolutional Neural Networks
KW - Cyber Attack Detection
KW - Logistic Regression
KW - Network Security
UR - http://www.scopus.com/inward/record.url?scp=85205963924&partnerID=8YFLogxK
U2 - 10.1109/AIoT63253.2024.00040
DO - 10.1109/AIoT63253.2024.00040
M3 - Conference contribution
AN - SCOPUS:85205963924
T3 - Proceedings - 2024 IEEE Annual Congress on Artificial Intelligence of Things, AIoT 2024
SP - 166
EP - 168
BT - Proceedings - 2024 IEEE Annual Congress on Artificial Intelligence of Things, AIoT 2024
T2 - 2024 IEEE Annual Congress on Artificial Intelligence of Things, AIoT 2024
Y2 - 24 July 2024 through 26 July 2024
ER -