DCNN-BIGRU: A Proficient Hybrid Classifier for Reliable Intrusion Detection and Prevention: Hybrid Approach
DOI:
https://doi.org/10.23055/ijietap.2025.32.1.10159Keywords:
Cybersecurity, IDS, Hybrid, Anomaly detection, ML, Network Attacks.Abstract
Advances in networking devices have revolutionized many industries by enabling intercommunication and automation in multiple areas, such as healthcare, transportation, and manufacturing. However, the threat of cyber-attacks has also escalated with the increased connectivity and dependency on these devices. Cyber security has become critical in protecting networks from malicious activities, ensuring the privacy and integrity of the data transmitted. Multiple deep-learning methods face multiple challenges in identifying intrusion threats; however, deep learning can self-enhance and scale up for reliability. We propose an efficient hybrid deep-learning intrusion-detection classifier, DCNN-BiGRU. The classifier has a simple architecture and works well in environments that do not require saving long-term dependencies and where computational resources are limited. It achieved a multiclass-classification accuracy of 99.70% on the training and test datasets.
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