Mitigating Generic Attacks for Intrusion Detetction System Based on CGAN and FIPSO Using UNSW-NB 15 Dataset
Keywords:
Distributed system, Deep Learning, Supervised learning, Network monitors traffic patterns, Intrusion detection systemAbstract
: In recent times, there has been a notable surge in cyberattacks due to the Internet of Things' exponential growth. Because of this, maintaining corporate borders today requires cybersecurity. Intrusion detection systems, or IDSs, are used to notify users of noteworthy events when maintaining a network. The first is the identification of malicious traffic, for which zero-day attack detection research is essential. This research provides an improved intrusion detection model that leverages FIPSO for feature extraction, conditional Generative Adversarial Networks (cGAN) to handle data imbalance, and machine learning techniques for classification tasks. We evaluated the model for binary and multi-classification, focusing on the UNSW-NB15 dataset in particular. The proposed methodology is noteworthy because it employs Random Forest (RF) classification along with FIPSO to enhance feature selection and cGAN to directly address the issue of data imbalance. This hybrid technique yields better results, with 83% accuracy in multi-class classification and 96% accuracy in binary classification.
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