Enhanced Network Intrusion Detection System Using PCGSO-Optimized BI-GRU Model in AI-Driven Cybersecurity

The detection of complex attacks by Network Intrusion Detection Systems (NIDS) is hindered by evasion strategies including encrypted traffic and polymorphic malware. Attackers frequently take advantage of holes in NIDS algorithms, emphasising the never-ending cat-and-mouse game between cybersecurity defences and dynamic attack tactics. In the context of cybersecurity, this study offers a sophisticated method for supporting Network Intrusion Detection Systems (NIDS). The tactic includes a thorough preprocessing stage that include functions for normalisation and standardisation in order to recover the accuracy and consistency of the input data. The Perceptive Craving Game Search Optimisation (PCGSO) algorithm is then used for feature selection, maximising the effectiveness of the NIDS. Bidirectional Gated Recurrent Unit (BI-GRU) representations are used in the classification phase because of their ability to identify sequential dependencies in network traffic data. A second PCGSO programme is used to carry out hyperparameter tuning, which guarantees the best possible model performance. The ISCXIDS2012, a popular benchmark dataset in the field, has been selected as the dataset for evaluation. The suggested approach demonstrates how PCGSO may be used to improve feature selection and hyperparameter tweaking, leading to an NIDS that is more accurate and resilient to cyberattacks. Performance evaluations and experimental findings show that the suggested technique outperforms other current models with 99% accuracy