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A Novel Intelligent Intrusion Prevention Framework for Network Applications

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Abstract

Nowadays, the intrusion prevention model in network applications is essential in protecting data from malicious users. The intrusion prevention model involves detecting and removing malicious events in the network. Although different prevention models have been developed in the past, there are still some issues with preventing malicious events and providing continuous monitoring. Hence, a novel hybrid prevention model named the Buffalo-based Elman neural model was proposed in this paper. Here, the input dataset, such as NSL-KDD and CICIDS, is trained and pre-processed to remove the noise features from the dataset. Also, feature extraction and attack classification are done to extract features from the dataset and neglect the malicious features. Moreover, continuous monitoring is provided in the network with the help of a login strategy. The designed model is implemented in a python environment, and the model's outcomes are validated. Finally, a comparative analysis is made by comparing the outcomes of the proposed model with other existing prevention models in terms of Accuracy, F-measure, error rate, execution time, recall, and precision. Comparative analysis shows that the designed intrusion prevention model achieved better outcomes than existing models. For NSL-KDD and CICIDS data, the proposed model achieved 98.4% and 99.7% accuracy.

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This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Vaagdevi College of Engineering, Bollikunta, Warangal, Telangana, India

    Rekha Gangula

  2. Department of Computer Science and Engineering, Kakatiya Institute of Technology and Science, Warangal, Telangana, India

    Sreenivas Pratapagiri, Sridhara Murthy Bejugama, Gayatri Nandam & Swapna Saturi

  3. Department of Information Technology, Kakatiya Institute of Technology and Science, Telangana, Warangal, India

    Sudharshan Ray

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Gangula, R., Pratapagiri, S., Bejugama, S.M. et al. A Novel Intelligent Intrusion Prevention Framework for Network Applications. Wireless Pers Commun 131, 1833–1858 (2023). https://doi.org/10.1007/s11277-023-10523-z

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