Abstract
IDS are crucial to network security because they can identify malicious activity and halt it in its tracks. Network intrusion data is often masked by a sea of benign data, making it difficult to train a model or perform a detection with a high FPR. This is because networks are inherently dynamic and change over time. In this research, we offer a ML & DL model-based method to ID, and we demonstrate how to deal with the issue of data imbalance by using a hybrid sampling technique. Conventional firewalls and data encryption technologies are unable to provide the level of security required by current networks. As a result, IDSs have been endorsed for use against network threats. Recent mainstream ID approaches have benefited from ML, but they have low detection rates & need a lot of feature engineering to be truly useful. Using layered CNN and Voting classifier (XGBoost and LGBM), this study introduces ML-DL-NIDS to address the issue of subpar detection precision. Using a publicly available NSL-KDD & UNSW-15 benchmark datasets for network intrusion detection, we find that this model outperforms competing methods according to accuracy and F1-score obtained from experimental evaluations.
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