An Intelligent Network Intrusion Detection System using Hybrid Machine Learning and Deep Feature Extraction

Network security has almost inflated to extremely high measures due to the high rates of growth of the number of interconnected devices, the cloud infrastructure and the high speed communication network. The conventional intrusion detection systems (IDS) are hardly able to cope with such big scale of traffic data, dynamic attacks and non-linear association amid different features, which leads to reduction in the detection rate, and enhancement of the rate of false-positive. In order to solve these issues, the paper is specialized in their solution and suggests the Intelligent Network Intrusion Detection System (INIDS) called Intelligent Network Intrusion Detection based on hybrid machine learning and deep feature extraction aimed at developing the strong and dynamic system of threat-detecting. The system utilizes highdimensional traffic data with the help of Autoencoders to reach helpful latent representations that have the ability to remove the noise and identify some significant pattern of behavior. The deep features are trained in a multi-configured scheme of the classifier consisting of the XGBoost, the Random Forest, and the Long Short-Term Memory (LSTM) networks. XGBoost and random forest have excellent ensemble level classification power on structured traffic characteristics compared to LSTM that acquires patterns of time trend and sequence of attacks. The hybrid integration in the model will add the power, the quality of generalization, quality of multi-class attack inference, and reduction of misclassification. The benchmark intrusion datasets used to show the experimental results showed that the proposed INIDS performs better in terms of detection accuracy, precision, recall, and F1-score better than the traditional IDS models. The findings prove the fact that deep feature extraction with the help of hybrid machine learning methods presents high performance in the sense of capability of determining higher features of cyber threats such as zero-day attacks. This framework suggests a nimble and intelligent framework that can be adopted in the contemporary cybersecurity models that need the tracking of threats on a real-time basis and in a continuous fashion.