Designing Secure Model to Classify IoT Vulnerabilities Using Binary and Multi Class Deep Neural Network Classification

Abstract

Excessive and exponential deployment of the Internet of Things (IoT) devices as wireless network makes it difficult to secure data. It is a high time to make IoT network vulnerable against various attacks. Since multiple vulnerabilities are simultaneously available in the IoT network, therefore, classifying the IoT vulnerabilities is multi-class problem. The paper contributed to design and develop a deep learning (DL) IoT security framework to classify and predict vulnerabilities. The principal component analysis (PCA) is used for feature set reduction and then rectified linear unit (ReLU) activation is used for eliminating vanishing gradient problem for binary classification. Further, the binary classification performance is compared with multi class classification models. Determining benignity or maliciousness of a sample is the main objective of detection models. The classification algorithms' objective is to assign each sample into one of the subsequent classes: beneficent, tsunami, Mirai, or Gafgyt. The classification of attacks is implemented using the deep neural network (DNN) based learning model. The N-BaIoT multiple vulnerabilities dataset is used which is of unbalanced nature. The paper suggested employing the oversampling approach Synthetic-Minority Oversampling Technique (SMOTE) for balancing the data set and compared performance with Random Oversampling and Undersampling (ROU) approach. Performance is compared with conventional decision tree, Random Forest (RF). The accuracy with proposed DNN approach is more than 99.99% in all cases.