Hybrid Deep Learning-Based Security Model for Robust Intrusion Detection in IoT Networks

The popularity of Internet of Things (IoT) devices has been responsible for a major growth in cybersecurity risks across sectors. This increasing complexity emphasizes the immediate need for more versatile and advanced intrusion detection systems. Our study defines a Hybrid Deep Learning-Based Security Model (HDLSM) meant to solve such problems by effectively distinguishing between possibly malicious and benign IoT network traffic using Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN). Training and validation of the model was done using the IoT23 dataset, which is a thorough set of real-world, labeled network data covering various malware attacks, including Mirai, Gafgyt, Tsunami, and Torii. To ensure the inputs were of the best quality, we conducted a thorough preprocessing stage including data cleaning, format standardization, and simplification of complex attributes. As we tested the HDLSM model, it achieved 96.6% accuracy, 96.6% precision, 96.1% recall, 96.3% F1 score, and 97.1% AUCROC.