An AI-Driven Based Cybersecurity System for Network Intrusion Detection System in Hybrid with EPO and CNNet-LAM

The proliferation of large data made possible by ubiquitous internet use has led to an uptick in cyberattacks, despite the proliferation of AI-based security keys like intrusion detection systems (IDS). Improved data classification is one benefit of the suggested detection system’s foundation in deep learning (DL) and Convolutional Neural Networks (CNNs). With the use of the IDSAI dataset, this research takes a close look at intrusion detection systems. Z-Score Normalisation and Min-Max Normalisation are used for data preparation. Picking out the most relevant characteristics from the preprocessed data is the next step after data preprocessing. As a result, the feature selection method makes use of an optimisation known as the Eagle Perching Optimisation (EPO) Algorithm. Convolutional Neural Networks with Long Short-Term Memory and Attention Mechanism (CNNet-LAM) are used for classification after selection. It is common practice to employ EPO during hyperparameter tweaking due to its efficacy. Classification issues may be effectively resolved by using the CNNet-LAM hybrid model. The suggested model consistently surpasses the competition, according to the testing data, and it can predict varying time delays with an accuracy of 99.31%.