Enhanced intrusion detection framework for securing IoT network using principal component analysis and CNN

Abstract

The Internet of Things (IoT) has transformed our world by connecting smart devices and enabling seamless interactions. This reliance, however, has led to new security issues and types of attacks. It is of the utmost importance to safeguard the security of IoT networks, with network intrusion detection systems (NIDS) having a significant impact. This paper proposes a novel approach integrating Principal Component Analysis (PCA), Pearson Correlation Coefficient (PCC), and Convolutional Neural Network (CNN) to overcome these security issues. Our innovative method reduces data dimensionality and selects highly correlated features using PCC and PCA, addressing overfitting and improving model performance while maintaining high computational speed and low costs. Our approach uniquely distinguishes between benign and threat packets by employing 1D-CNN, 2D-CNN, and 3D-CNN algorithms trained on Edge-IIoTset and NSL-KDD benchmark datasets. The findings from our experiments indicate that the proposed framework significantly enhances accuracy, precision, recall, and F1-score compared to existing models for both binary and multiclass classifications. Our binary classification models achieved exceptional performance, with an average accuracy of 99.76%, 99.79% precision, 99.89% recall, and 99.85% F1-score on the Edge-IIoTset dataset. On the NSL-KDD dataset, the models attained 99.20% accuracy, 98.07% precision, 97.95% recall, and 97.71% F1-score. For multiclass classification, the proposed model demonstrated an average accuracy of 99.41%, precision of 98.61%, recall of 98.49%, and an F1-score of 98.56% on the Edge-IIoTset dataset. On the NSL-KDD dataset, the model achieved 92.43% accuracy, 93.21% precision, 93.60% recall, and a 93.7% F1-score. Our research introduces a significant advancement that substantially improves NIDS capabilities, making IoT networks safer and more connected.

Topics & Concepts