Detecting Network Intrusions Using Big Data-Driven Artificial Intelligence Techniques in Cybersecurity
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I3P106Keywords:
Cybersecurity, NSL-KDD dataset, Network intrusion, machine learning, CNN ModelAbstract
Cyberattacks are becoming more sophisticated, so protecting contemporary networks requires intrusion detection systems (IDS) that are both effective and intelligent. This study proposes a Convolutional Neural Network (CNN)-based model for detecting intrusions using the NSL-KDD dataset, leveraging deep learning's ability to automatically extract hierarchical features from complex network traffic patterns. The model underwent rigorous evaluation through performance metrics including precision, accuracy, recall, and F1-score. According to the results, the suggested CNN has an astonishingly high rate of accuracy of 99.9%, the model far surpasses conventional machine learning methods like Naïve Bayes and Artificial Neural Networks (ANN), and Multi-Layer Perceptron (MLP). These findings validate the strength of CNN in capturing intricate behaviors in network data, making it an attractive option for immediate and large-scale cybersecurity applications. Furthermore, the model demonstrates strong generalization, low error rates, and minimal overfitting, proving its robustness in handling diverse intrusion types. For use in future research that aims to increase detection accuracy and flexibility, they will be using hybrid models and updating Their datasets
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