An optimized model for network intrusion detection in the network operating system environment
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Abstract
With the heavy reliance on computers and information technology to send and receive data across networks of various types, there has been concern about securing that data from intrusions and cyber-attacks. The expansion of network usage has led to an increase in hacker attacks, which has led to prioritizing cybersecurity precautions in detecting potential threats. Intrusion detection techniques are a critical security measure to protect networks in both personal and corporate environments that are managed by network operating systems. For this, the paper relies on designing a network intrusion detection model. Since deep neural networks (DNNs) are classic deep learning models known for their strong classification performance, making them popular in intrusion detection along with other machine learning algorithms, they have been chosen to improve intrusion classification models based on datasets for intrusion detection systems. The basic structure of this proposal is to adopt one of the optimization algorithms in extracting features from the dataset to obtain more accurate results in the classification and intrusion detection stage. The developed Corona Virus algorithm is adopted to improve the system performance by identifying optimal features. This algorithm, which consists of several stages, optimally selects individuals based on features from the NSL-KDD dataset used for intrusion detection. The resulting optimization solution acts as a network structure for the intrusion classification model based on machine learning and deep learning algorithms. The test results showed exceptional performance on the NSL-KDD dataset, where the proposed Convolution Neural Network CNN model achieved 99.3% accuracy for multi-class classification, while the Decision Tree (DT) achieved 88.64% accuracy for anomaly detection in bi-class classification.
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