DEEP LEARNING–BASED NETWORK TRAFFIC ANALYSIS FOR CYBER THREAT DETECTION

Abstract

With the rapid expansion of digital networks and cloud-based infrastructures, cyber threats have become increasingly sophisticated and difficult to detect using traditional rule-based security mechanisms. Conventional intrusion detection systems (IDS) often fail to recognize zero-day attacks and advanced persistent threats due to their reliance on predefined signatures and handcrafted features. To address these limitations, this paper proposes a deep learning–based framework for intelligent network traffic analysis and cyber threat detection. The proposed approach leverages deep neural architectures to automatically learn complex patterns from raw network traffic data, enabling accurate detection of both known and unknown attacks. Experimental evaluation using benchmark datasets demonstrates that the proposed model achieves superior detection accuracy, reduced false-positive rates, and improved generalization compared to traditional machine learning techniques. The results highlight the effectiveness of deep learning in enhancing network security and building robust, adaptive cyber defense systems.