Leveraging deep Learning for Efficient Intrusion Detection in IoT Networks

Internet of Things (IoT) technology is experiencing rapid development and increasing use in a variety of applications, making it a potential target for cyber-attacks. Machine learning and deep neural network techniques are an effective way to address these challenges and improve IoT security. This research aims to design a deep learning techniques for intrusion detection in an Internet of Things environment with limited resources. The research focuses on improving the efficiency and effectiveness of current model using artificial intelligence and LSTM algorithms, ensuring reliable and effective security in the IoT environment. The proposed model is evaluated using a realistic data set, Canadian Institute for Cybersecurity Internet of Things 2023 Dataset (CICIoT2023) devices, and using performance metrics, namely Accuracy, Precision, F1 Score, and Recall. The results show its compatibility and effectiveness in a real environment, with 99.1% accuracy recorded. This paper is considered an important contribution to the field of IoT security and provides an effective methodology for developing advanced security solutions in the IoT environment that enhance traffic analysis, identify abnormal behavior, and take the necessary measures.