Abstract
In the ever-evolving landscape of modern education systems, the integration of technology has become ubiquitous, opening new avenues for teaching and learning. However, this increased reliance on digital platforms has also given rise to unprecedented cybersecurity challenges, necessitating advanced detection mechanisms to safeguard sensitive educational data. This book chapter explores the application of deep learning approaches for intelligent cyber threats detection in the context of the modern education system. The chapter begins by providing a comprehensive overview of the evolving cyber threat landscape within educational institutions, highlighting the diverse range of attacks targeting student records, intellectual property, and critical infrastructure. It emphasizes the need for proactive and adaptive cybersecurity measures to counteract these threats effectively. Subsequently, the chapter delves into the foundational principles of deep learning, elucidating its capacity to autonomously learn intricate patterns and anomalies from vast datasets. Various deep learning architectures, such as convolutional neural networks and recurrent neural networks are discussed in the context of their applicability to cybersecurity in education. The practical implementation of deep learning models for cyber threats detection is then explored. Case study that illustrate how these models can analyze detect malware, and identify suspicious activities, thereby fortifying the resilience of educational systems against cyber threats. In conclusion, this book chapter provides a comprehensive exploration of deep learning approaches as a potent tool for intelligent cyber threats detection in modern education systems.
