Enhancing IoT Security with Deep Stack Encoder using Various Optimizers for Botnet Attack Prediction

Abstract

The Internet of Things (IoT) connects different sensors, devices, applications, databases, services, and people, bringing improvements to various aspects of our lives, such as cities, agriculture, finance, and healthcare. However, guaranteeing the safety and confidentiality of IoT data which has become rich in its quality requires careful preparation and awareness. Machine learning techniques are used to predict different types of cyber-attacks, including denial of service (DoS), botnet attacks, malicious operations, unauthorized control, data probing, surveillance, scanning, and incorrect setups. In this study, for improving security of IoT data, a method called Deep Stack Encoder Neural Network to predict botnet attacks by using N-BaIoT bench mark dataset is employed. In this study a new framework is introduced which will improve the performance of prediction rate to 94.5%. To evaluate the performance of this method assessment criteria are adopted like accuracy, precision, recall, and F1 score, comparing it with other models. From the optimizers of Adam, Adagrad and Adadelta, Adam optimizer gave the highest accuracy with relu activation function.