Deep Learning for Intrusion Detection in IoT Networks

Abstract

The rapid proliferation of Internet of Things (IoT) devices has transformed our daily lives, introducing innovations like smart homes, wearables, and advanced industrial automation. While these interconnected systems offer convenience and efficiency, they also present significant security challenges. With the expansion of the IoT network comes an increased risk of malicious attacks, making safeguarding these networks a pressing concern. Intrusion detection serves as a crucial defense mechanism, detecting abnormal activities and triggering appropriate responses. In our study, we harness the power of ensemble learning through a technique known as bagging. By combining the strengths of Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs), we aim to capitalize on their unique advantages and enhance the overall capability of intrusion detection systems.