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<p>Achieving cyber-security has grown more challenging in recent years. There is a need for a strong defense system against various cyberattacks to protect the data from the threat and damage. We have developed the hybrid model based on crow search optimization (CSO) and random forest (RF) algorithm for identifying threats and irregularities in a computer network. In comparison to the various state-of-the-art classifiers, the CSO method was applied to the NSL-KDD dataset and the ROSPaCe dataset for the detection of the attacks. In the proposed work, we have used a random forest technique to perform feature selection (FS) to improve the effectiveness and efficiency of intrusion detection. We have also created a confusion matrix over different kinds of attacks concerning predicted class and attack class, enabling us to evaluate the performance of a classification model for various attack groups without taking CSO and after applying the CSO approach. In this study, the various performance matrix parameters have been evaluated in terms of all classifiers along with the proposed model. In the NSL-KDD dataset, the proposed model offers 88.48% accuracy for binary distribution and 86.92% accuracy for multiclass distribution, and the detection rate of DoS attack is 99.9%, Probe is 97.8%, R2L is 99.7%, and U2R is 97.1% respectively. In the ROSPaCe dataset, the accuracy is 98.6%, and in the CICIoT2023 dataset, the accuracy is 99.92%. As compared to the previous models like NB Tree, Naïve Bayes, SVM, and other previous models, the implemented RF-CSO offers better detection accuracy and a low false alarm rate compared in the result section.</p>

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Intelligent intrusion detection system based on crowd search optimization for attack classification in network security

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月11日

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  • Chetan Gupta,
  • Amit Kumar,
  • Neelesh Kumar Jain

摘要

Achieving cyber-security has grown more challenging in recent years. There is a need for a strong defense system against various cyberattacks to protect the data from the threat and damage. We have developed the hybrid model based on crow search optimization (CSO) and random forest (RF) algorithm for identifying threats and irregularities in a computer network. In comparison to the various state-of-the-art classifiers, the CSO method was applied to the NSL-KDD dataset and the ROSPaCe dataset for the detection of the attacks. In the proposed work, we have used a random forest technique to perform feature selection (FS) to improve the effectiveness and efficiency of intrusion detection. We have also created a confusion matrix over different kinds of attacks concerning predicted class and attack class, enabling us to evaluate the performance of a classification model for various attack groups without taking CSO and after applying the CSO approach. In this study, the various performance matrix parameters have been evaluated in terms of all classifiers along with the proposed model. In the NSL-KDD dataset, the proposed model offers 88.48% accuracy for binary distribution and 86.92% accuracy for multiclass distribution, and the detection rate of DoS attack is 99.9%, Probe is 97.8%, R2L is 99.7%, and U2R is 97.1% respectively. In the ROSPaCe dataset, the accuracy is 98.6%, and in the CICIoT2023 dataset, the accuracy is 99.92%. As compared to the previous models like NB Tree, Naïve Bayes, SVM, and other previous models, the implemented RF-CSO offers better detection accuracy and a low false alarm rate compared in the result section.