Deep Learning based Ensemble Model for Intrusion Detection in IoT Network

The fast-growing development of IoT devices brings along many security challenges, as the more connected devices become easier prey for different types of cyberattacks: DoS, botnet, data exfiltration, and spoofing attacks. Traditional intrusion detection systems (IDS) find it hard to deal with the complexity and limited resources involved in IoT settings. Most standard IDS systems work based on signature generation, either through signature-based or anomaly-based systems, though practically all face difficulties adapting to novel attack patterns and giving large false positive counts. An Ensemble Deep Learning (EDL) based intrusion detection system for IoT networks is proposed that combines architectures of Transformer, Long Short-Term Memory (LSTM) networks, and Convolutional Neural Networks (CNNs) into its intrusion detection module. These models provide more contextual insight into the attack by employing transformers, which are analyzed through LSTM to investigate temporal patterns, and CNNs can extract spatial features from the network traffic, thus allowing for an efficient and diverse anomaly detection mechanism. The IDS using EDL outperforms independent deep learning models, providing higher detection accuracy, reduced false positive rates, and greater adaptation to new threats, as shown by an analysis of the CICIoT2023 dataset comprising a diverse set of IoT attack scenarios. This approach is perfect for securing modern IoT systems as it provides an efficient and reliable intrusion detection system. Real-time deployment and resource-limited device optimization will be the key points of future development.