International Research Journal of Education and Technology

The fast growth of the Internet of Things (IoT) has brought about unparalleled ease and effectiveness, while simultaneously posing notable security obstacles. Cyber-attacks on IoT devices can cause serious privacy breaches, disrupt vital systems, and potentially lead to cascading failures across interconnected networks. As IoT devices become more integrated into critical infrastructure, the need for robust security measures has become increasingly urgent. This paper seeks to create a forecasting model using advanced deep learning methods to identify and stop possible cyber assaults in IoT networks. The method suggested leverages comprehensive datasets comprising information from different IoT gadgets, such as network traffic patterns, device activity logs, and environmental factors, to detect abnormalities that could indicate a forthcoming attack. By employing a deep learning platform that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, the model is crafted to understand, adapt, and evolve in response to changing risk landscapes. Additionally, the system incorporates an anomaly detection module that continuously learns from new data, enhancing its ability to predict novel and sophisticated attack vectors. The model's efficacy is confirmed by thorough testing on multiple benchmark datasets, and its performance is rigorously compared to traditional security approaches. The findings show that the suggested deep learning system not only greatly enhances the early identification and mitigation of cyber threats but also provides a scalable and flexible framework that can be adapted to different IoT environments. This research contributes a strong and innovative method to boost IoT security, ensuring the reliability and safety of interconnected devices in an increasingly digital world.

Keywords- Cyber Attack Prediction, IoT, Deep Learning, Anomal y Detection, Network Security, CNN, Threat Mitigation LSTM, IoT Security Framework.