Year: 2025
Type: article
Abstract: : Cyber threats are rapidly evolving, demanding sophisticated defense mechanisms. This paper presents a novel cyber threat detection system leveraging Artificial Neural Networks (ANNs) to enhance intrusion detection capabilities. The system incorporates data preprocessing techniques, including feature scaling, encoding, and dimensionality reduction, to optimize ANN performance. A state-of-the-art ANN model is employed to analyze network traffic, identify anomalies, and detect intrusions in real-time. The system integrates with MongoDB for efficient data storage and retrieval, facilitating further analysis and reporting. By harnessing the adaptability and learning capabilities of ANNs, the proposed system aims to provide a robust, scalable, and efficient solution for detecting emerging cyber threats while minimizing false positives. The results demonstrate significant improvements in detection accuracy and response time compared to traditional methods, contributing to the advancement of AIpowered cybersecurity solutions.
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4409476993