Intelligent Deep Learning-based Systems to Detect Early Cyber Attacks in IoT Networks

Abstract

The Internet of Things (IoT) has changed the way modern devices connect. Billions of smart devices now work together in healthcare, transportation, factories, smart homes, and critical infrastructure. But IoT devices are small and often under-resourced. This makes them easy targets for serious attacks such as DDoS, botnets, spoofing, ransomware, and data breaches. Most intrusion detection systems (IDSs) still rely on signature or basic machine learning. These methods make it difficult to detect new or unknown attacks in rapidly changing IoT settings. The study develops a deep learning system that can detect cyber attacks early in IoT networks. The design includes a complete data processing stage, feature normalization, and a hybrid deep learning model. The model itself can find spatial and temporal patterns in network traffic. It uses bypass neural networks (CNNs) to extract features and learn from sequences using short-term long-term memory networks (LSTM). Together, they provide high recognition accuracy with low false alarm. The framework was tested on a public IoT penetration dataset. Accuracy, accuracy, memory, F1 score, and receiver undercrew operation (ROC-AUC) were verified. The results show that this method separates natural motion from different types of attacks with high accuracy. This makes it suitable for real-time use. Overall, deep learning intrusion detection provides a measurable, flexible, and effective way to protect IoT systems from ever-changing threats.