An efficient intrusion detection system for IoT security using CNN decision forest

被引：0
作者：
Bella, Kamal [1 ]
Guezzaz, Azidine [1 ]
Benkirane, Said [1 ]
Azrour, Mourade [2 ]
Fouad, Yasser [3 ]
Benyeogor, Mbadiwe S. [4 ]
Innab, Nisreen [5 ]
机构：
[1] Technology Higher School Essaouira, Cadi Ayyad University, Essaouira
[2] IDMS Team, Faculty of Sciences and Technics, Moulay Ismail University of Meknès, Errachidia
[3] Department of Applied Mechanical Engineering, College of Applied Engineering, King Saud University, Muzahimiyah Branch, Riyadh
[4] Institute of Physics, University of Munster, Munster
[5] Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, Riyadh
来源：
PeerJ Computer Science | 2024年 / 10卷
关键词：
Deep learning; Intrusion detection; IoT; Machine learning; Security;
D O I：
10.7717/PEERJ-CS.2290
中图分类号：
学科分类号：
摘要：
The adoption and integration of the Internet of Things (IoT) have become essential for the advancement of many industries, unlocking purposeful connections between objects. However, the surge in IoT adoption and integration has also made it a prime target for malicious attacks. Consequently, ensuring the security of IoT systems and ecosystems has emerged as a crucial research area. Notably, advancements in addressing these security threats include the implementation of intrusion detection systems (IDS), garnering considerable attention within the research community. In this study, and in aim to enhance network anomaly detection, we present a novel intrusion detection approach: the Deep Neural Decision Forest-based IDS (DNDF-IDS). The DNDF-IDS incorporates an improved decision forest model coupled with neural networks to achieve heightened accuracy (ACC). Employing four distinct feature selection methods separately, namely principal component analysis (PCA), LASSO regression (LR), SelectKBest, and Random Forest Feature Importance (RFFI), our objective is to streamline training and prediction processes, enhance overall performance, and identify the most correlated features. Evaluation of our model on three diverse datasets (NSL-KDD, CICIDS2017, and UNSW-NB15) reveals impressive ACC values ranging from 94.09% to 98.84%, depending on the dataset and the feature selection method. Notably, our model achieves a remarkable prediction time of 0.1 ms per record. Comparative analyses with other recent random forest and Convolutional Neural Networks (CNN) based models indicate that our DNDF-IDS performs similarly or even outperforms them in certain instances, particularly when utilizing the top 10 features. One key advantage of our novel model lies in its ability to make accurate predictions with only a few features, showcasing an efficient utilization of computational resources. Copyright 2024 Bella et al. Distributed under Creative Commons CC-BY 4.0
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