CYBER THREAT DETECTION USING DEEP LEARNING

Abstract

With the rapid growth of digital systems and internet connectivity, cyber threats have become increasingly sophisticated and frequent, posing significant risks to individuals, organizations, and critical infrastructure. Traditional signature-based detection methods often fail to identify new or evolving attacks, highlighting the need for intelligent, adaptive security solutions. This study proposes a deep learning-based cyber threat detection system that leverages neural networks to automatically learn complex patterns from network traffic and system logs, enabling the identification of anomalies and malicious activities in real-time. The model combines feature extraction, classification, and predictive analytics to detect threats such as malware, ransomware, phishing, and distributed denial-of-service (DDoS) attacks with high accuracy. Experimental evaluations demonstrate that the proposed system outperforms conventional methods in detecting both known and zero-day attacks, providing a robust framework for proactive cybersecurity.