Intelligent Cyber Threat DetectionUsing Deep Learning and Machine Learning Techniques

Abstract

The increasing use of digital networks, cloud platforms, mobile devices, and online services has created new opportunities for cybercriminals to perform sophisticated attacks. Traditional cybersecurity systems mainly depend on predefined rules and known attack signatures, which can make them less effective against new and rapidly changing threats. Machine learning and deep learning provide intelligent approaches that can analyze large amounts of network data and identify unusual or malicious patterns automatically. This research presents an intelligent cyber threat detection approach that uses machine learning and deep learning techniques to identify cyberattacks and suspicious network activities. The proposed approach includes data collection, preprocessing, feature selection, model training, threat classification, and performance evaluation. Several machine learning algorithms, including Logistic Regression, Support Vector Machine, Random Forest, and XGBoost, are considered and compared with a Deep Neural Network. The experimental evaluation is based on a modern intrusion detection dataset containing both normal and malicious network traffic. The results indicate that ensemble machine learning methods provide strong detection performance, while the Deep Neural Network achieves the highest overall performance among the evaluated approaches.