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Intrusion detection using ensemble learning and deep learning for IoT network security
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AI
DOI：10.1080/19393555.2026.2632670.png)
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Abstract
En 中文
The Internet of Things (IoT) has emerged as a crucial expansion of Internet services, linking numerous devices that produce and exchange substantial quantities of data. Strong defense against evolving cyberattacks that jeopardize the confidentiality, integrity, and availability (CIA Triad) of IoT systems is necessary due to this interconnectedness. Therefore, by controlling network activity to stop invasions and identify odd data patterns, intrusion detection systems can offer a robust layer of security for IoT systems. With a focus on identifying less frequent Advanced Persistent Threats (APT), this study aims to detect attacks directed at the CIA Triad. We examined two popular and unbalanced data sets, TON-IoT and BoT-IoT, for anomaly detection in IoT networks. By examining the usefulness of the ensemble learning and deep learning models in anomaly detection inside the IoT network data, we were able to study the security element of the network. The ensemble learning models considered are the following. Random Forest, XGBoost, and Stacking. The considered deep learning models are as follows: BiLSTM, GRU, Autoencoder, and Self-Taught Learning (STL: AE-SVM). The performance metrics studied are as follows: Accuracy, False Alert Rate, Recall, Precision, and F1-score. Regrettably, there were instances in which anomalies could not be detected using conventional techniques. This is because labeled data is scarce, particularly for APT attacks, and attack traffic is frequently sparse and noisy. We suggested a deep self-taught learning system (STL: AE-XGB) based on Transfer Learning to detect anomalies in IoT network data to tackle these difficult issues. To enhance classification performance and capture the changing landscape of attack methods in IoT networks. Our models automatically augment annotated data. The results show that our proposed model (STL: AE-XGB) outperforms the conventional models studied and provides interesting results in binary and multiclass classification.
Keywords:
Deep learning (DL)
ensemble learning (EL)
internet of things (IoT)
intrusion detection system (IDS)
IoT network
Journal
I
IF:
1.4
Papers:
39
Citations:
0