A Deep Learning-Based Framework for Real-Time Detection of Cybersecurity Threats in IoT Environments

The rapid adoption of Internet of Things (IoT) devices has led to an exponential increase in cybersecurity threats, necessitating efficient and real-time intrusion detection systems (IDS). Traditional IDS and machine learning models struggle with evolving attack patterns, high false positive rates, and computational inefficiencies in IoT environments. This study proposes a deep learning-based framework for real-time detection of cybersecurity threats in IoT networks, leveraging Transformers, Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) architectures. The proposed framework integrates hybrid feature extraction techniques, enabling accurate anomaly detection while ensuring low latency and high scalability for IoT devices. Experimental evaluations on benchmark IoT security datasets (CICIDS2017, NSL-KDD, and TON_IoT) demonstrate that the Transformer-based model outperforms conventional IDS solutions, achieving 98.3% accuracy with a false positive rate as low as 1.9%. The framework also incorporates adversarial defense mechanisms to enhance resilience against evasion attacks. The results validate the efficacy, adaptability, and real-time applicability of the proposed deep learning approach in securing IoT networks against cyber threats.