An Intelligent Cyber Threat Detection Framework Using a Hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) Machine Learning Mode

Abstract

The attack surface for contemporary cyber threats has greatly increased due to the growing reliance on networked digital systems and internet-driven services. Conventional security solutions that rely on static signatures and predetermined rules are no longer adequate as cyberattacks become more sophisticated and adaptable. Due to these conventional methods' inability to detect new and zero-day threats, network infrastructures are more vulnerable and detection is delayed. This paper offers an intelligent cyber threat detection framework based on a hybrid deep learning architecture that combines Long Short-Term Memory (LSTM) networks with Convolutional Neural Networks (CNN) in order to get around these restrictions. The suggested system does away with the requirement for human feature engineering by using the CNN module to automatically learn and extract significant spatial patterns from network traffic features. The detection of intricate and dynamic attack patterns is therefore made possible by using the LSTM module to represent the temporal linkages and sequential behaviors found in network traffic flows. Widely used intrusion detection datasets are used to verify the efficacy of the suggested model. Standard criteria, such as accuracy, precision, recall, F1-score, and false positive rate, are used to evaluate performance. According to experimental results, the hybrid CNN–LSTM architecture regularly outperforms both individual deep learning models and traditional machine learning techniques in terms of detection performance. The proposed approach demonstrates strong generalization capability and is well suited for real-time deployment in dynamic cybersecurity environments.