Deep Learning for Detecting Cyber Threats in Indian Government Networks

Abstract

The increasing digitization of government services in India has led to a surge in cyber threats, necessitating advanced solutions for cybersecurity. This paper examines the application of deep learning models, including Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, for detecting and mitigating cyber threats in Indian government networks. The study evaluates the performance of these models in detecting malware, ransomware, and phishing attacks by analyzing large datasets of network traffic. A key focus is on the scalability of these models to handle vast amounts of real-time data while maintaining high detection accuracy. The paper also highlights the challenges of implementing deep learning-based cybersecurity solutions in government networks, such as data privacy concerns, the complexity of infrastructure, and the need for policy frameworks. By presenting case studies of successful implementations in Indian government agencies, the paper showcases the potential of deep learning to revolutionize cybersecurity in the public sector.