International Research Journal of Education and Technology

With the rapid expansion of digital infrastructure, cyber threats have become increasingly sophisticated, frequent, and damaging. Traditional cybersecurity systems focus primarily on detection and response, often reacting after an attack has already occurred. This reactive approach is insufficient in today’s dynamic threat landscape. A Cyber Attack Predictor aims to shift cybersecurity strategies from reactive defense to proactive prevention by leveraging machine learning, artificial intelligence, and data analytics to forecast potential cyber threats before they materialize. This journal presents a comprehensive study on Cyber Attack Prediction systems, including their architecture, methodologies, data sources, predictive models, evaluation techniques, and real-world applications. It discusses how predictive analytics, anomaly detection, and deep learning models such as Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNNs), and Transformer-based models can be used to anticipate attacks like Distributed Denial of Service (DDoS), phishing, ransomware, insider threats, and zero-day exploits. The paper also highlights challenges, ethical considerations, and future research directions in  predictive cybersecurity.