Research on hybrid intrusion detection based on improved Harris Hawk optimization algorithm

Zhou, Peng-Zhen; Zhang, Huifu; Liang, Wei

doi:10.1080/09540091.2023.2195595

Cited by 7 publications

(2 citation statements)

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“…The better outcomes are obtained in terms of sensitivity, accuracy, and precision while compare to the techniques used in [28] and [31]. The Deep neural network approach [32] provided less accuracy and precision of 86.79% and 89.46% on NSL-KDD and 81.92%, 82.75% on UNSW-NB15, respectively. The proposed technique provided better results in accuracy, sensitivity, specificity, precision, and F1-score and better computational efficiency compared to the techniques described in the literature.…”

Section: Comparative Analysismentioning

confidence: 94%

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Network intrusion classification for IoT networks using an extreme learning machine

Akuthota,

Bhargava

2024

Eng. Res. Express

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The detection of intrusions has a significant impact on providing information security, and it is an essential technology to recognize diverse network threats effectively. This work proposes a machine learning technique to perform intrusion detection and classification using multiple feature extraction and testing using an Extreme learning machine (ELM). The model is evaluated on the two network intrusion datasets (NSL-KDD and UNSW-NB15), which consist of real-time network traffic. The arithmetic, gradient, and statistical features were extracted and evaluated with the proposed model. The method's efficacy is assessed using accuracy, sensitivity, specificity, precision, and F1-score. The proposed method achieves 94.5%, 97.61%, 96.91%, 96.51%, and 97.05% accuracy, sensitivity, specificity, precision, and F1-score for NSL-KDD and 94.3%, 98,36%, 99.31%, 99.67% and 99.01% of accuracy, sensitivity, specificity, precision and F1-score for the UNSWNB-15 dataset respectively, which is better performance outcomes when compared to other existing works.

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Section: Comparative Analysismentioning

confidence: 94%

“…Zhou et al [32] proposed a more effective hybrid Harris Hawk intrusion detection technique to increase discovery capacity. The enhanced Harris Hawk optimization method is utilized as a selection of feature strategy for the negative effects terminated and noisy structures on the presentation of the grouping method.…”

Section: Prior Workmentioning

confidence: 99%