Anomaly-based intrusion detection system for IoT application

Abstract

Internet-of-Things (IoT) connects various physical objects through the Internet and it has a wide application, such as in transportation, military, healthcare, agriculture, and many more. Those applications are increasingly popular because they address real-time problems. In contrast, the use of transmission and communication protocols has raised serious security concerns for IoT devices, and traditional methods such as signature and rule-based methods are inefficient for securing these devices. Hence, identifying network traffic behavior and mitigating cyber attacks are important in IoT to provide guaranteed network security. Therefore, we develop an Intrusion Detection System (IDS) based on a deep learning model called Pearson-Correlation Coefficient - Convolutional Neural Networks (PCC-CNN) to detect network anomalies. The PCC-CNN model combines the important features obtained from the linear-based extractions followed by the Convolutional Neural Network. It performs a binary classification for anomaly detection and also a multiclass classification for various types of attacks. The model is evaluated on three publicly available datasets: NSL-KDD, CICIDS-2017, and IOTID20. We first train and test five different (Logistic Regression, Linear Discriminant Analysis, K Nearest Neighbour, Classification and Regression Tree,& Support Vector Machine) PCC-based Machine Learning models to evaluate the model performance. We achieve the best similar accuracy from the KNN and CART model of 98%, 99%, and 98%, respectively, on the three datasets. On the other hand, we achieve a promising performance with a better detection accuracy of 99.89% and with a low misclassification rate of 0.001 with our proposed PCC-CNN model. The integrated model is promising, with a misclassification rate (or False alarm rate) of 0.02, 0.02, and 0.00 with Binary and Multiclass intrusion detection classifiers. Finally, we compare and discuss our PCC-CNN model in comparison to five traditional PCC-ML models. Our proposed Deep Learning (DL)-based IDS outperforms traditional methods.

A Hybrid Modified Deep Learning Architecture for Intrusion Detection System with Optimal Feature Selection

“…The comparison graph for the same is given in Figure 3. The graph reveals that the standard GRU-RNN [45] model shows the least values for accuracy (89%) followed by DBN [45], LDA [46] with an accuracy of (92.66%) and (93%), whereas it was 98.72% as a maximum value with the standard HFS-LGBM [47] model. However, this accuracy value was outperformed by the proposed model, which attained the highest accuracy of 99.967%, an improvement of 1.247% compared to the best-performing standard model (HFS-LGBM).…”

Firefly algorithm based WSN-IoT security enhancement with machine learning for intrusion detection

“…In the work presented in 24 , a novel deep learning model known as Pearson-Correlation Coefficient—Convolutional Neural Networks (PCC-CNN) is introduced. This model is designed for the purpose of identifying anomalies within networks.…”

Ensemble Machine Learning Approach for Iot Intrusion Detection Systems

“…In [9], an IDS called Pearson correlation coefficientconvolutional neural networks (PCC-CNN) was established for the deep learning model. Intrusion detection was performed by collecting features, detecting changes, and extracting linear operations.…”