A Hybrid CNN-LSTM-PSO Framework for Enhanced Cybersecurity Threat Detection and Classification
S. Akilandeswari, T. R. Soumya, S. Sumathi, Mishmala Sushith, V. Brinda, S. Rajan · 2025
With the advancement of digital era comes an increased value for cybersecurity, and these need to be dealt with using advanced techniques. Deep learning — metaheuristic optimization hybrid model for advanced threat detection and mitigation is presented in this paper. With a Convolutional Neural Network (CNN) for feature extraction and Long Short-Term Memory (LSTM) for sequence analysis, the proposed framework integrates all the modules in order to provide robust cyber-attack detection. In order to optimize the parameters of the model, a Particle Swarm Optimization (PSO) algorithm is used in order to achieve better classification accuracy and faster computation. Benchmark datasets are used for extensive experiments, where existing methods outperformed in terms of precision, recall, and Fl-score. The results demonstrate that with detected flagged FPs and the hybrid model, the increased detection rates are achieved without the reduction in true negatives, rendering it a practical solution for real world applications. This research adds to the field by providing a solution to cybersecurity problems that is scalable and adaptable to the present and beyond.