SmartSentry: Cyber Threat Intelligence in Industrial IoT

Abstract

While the Internet of Things (IoT) paradigm has transformed connectivity, it has also brought with it previously unheard-of security risks. The categorization of IoT attacks using several machine learning techniques and a deep learning method is the main emphasis of this research. In addition to proposing a binary and multiclass classification framework with Machine Learning (ML) algorithms like Random Forest (RF), Decision tree (DT), Extra Tree Classifier (ETC), Support Vector Machine (SVM), and k-Nearest Neighbor (KNN) and Deep Learning (DL) architectures like Deep Neural Network (DNN), the study assesses a wide range of attack types in IoT environments. Benchmark datasets with real-world IoT attack scenarios, such as Edge-IIoTset, are used for experimentation. Preprocessing is done on the dataset using Principal Componenet Analysis (PCA) for feature selection, Synthetic Minority Oversampling Technique to handle class imbalance and Standard Scaling for feature scaling. These approaches’ comparative performance and efficacy are examined. The outcomes indicate how successful the DL model in managing intricate attack patterns and the generalization capabilities of ML algorithms across various attack classes. The DNN model yields the best results, with 100% accuracy for binary classification, 96.15% accuracy for 6-class classification, and 94.68% accuracy for 15-class classification. Further, 10-fold cross validation has been applied to make sure that the model does not overfit. This work contributes to the improvement of IoT security mechanisms by offering insights into the selection of appropriate approaches for binary and multiclass classification of threats.

Keywords

• Internet of Things
• machine learning
• security
• intrusion detection system