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Providing a Consistent Method to Model the Behaviour and Modelling Intrusion Detection Using Hybrid Particle Swarm Optimization-Logistic Regression Algorithm

ABSTRACT

An intrusion detection system is a collection of instruments, methods, and documentation that aid in identifying, determining, and reporting unwanted or illegal network activity. Intrusion detection systems build as software and hardware systems, each with its own set of benefits and drawbacks. Because of the intrusion detection system's nonlinearity and nonstationary, the correctness of traditional methods, such as regression analysis and neural networks, was limited.. In this research, a regression-based prediction model is proposed to handle an intrusion detection behavior problem.To develop an effective regression model, the parameters must be carefully adjusted. This present research introduces a hybrid methodology called real-value particle swarm optimization algorithm regression (RPSO), which uses real-value particle swarm optimization algorithms to find the optimal parameters. Then, it uses the best parameters to build the regression models. The method is used to forecast the data related to intrusion detection behaviour from the VIRUS TOTAL Dataset. Due to the Root Mean Square Error (RMSE) and mean absolute percentage error, the experimental results show that RPSO performs best the standard regression and back-propagation (BP) neural network models (MAPE). .It was proved that the RPSO model is a practical method to recognize the behavior of the Intrusion detection System Feature.

کامنت های مجله :

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Report

Reviewer 1

09.11.2021

1-Abstract section needs improvements by adding quantitative performance of the proposed PSO.  

2-Introduction section needs to be re-organized. Authors are strongly recommended to improve introduction in terms of motivation, novelty, and research organization (last paragraph of introduction section). 

3-Literature review needs improvements by using following applications of PSO applications into practical views:

Neuro-fuzzy GMDH based particle swarm optimization for prediction of scour depth at downstream of grade control structures

Application of improved neuro-fuzzy GMDH to predict scour depth at sluice gates

Neuro-fuzzy GMDH approach to predict longitudinal dispersion in water networks

Neuro-fuzzy GMDH systems based evolutionary algorithms to predict scour pile groups in clear water conditions

Neuro-fuzzy GMDH-based evolutionary algorithms to predict flow discharge in straight compound channels

4-Figure 2 needs redrawing due to poor quality.

5-Tables 1&2 should be combined together. In terms of quantitative comparisons, authors are strongly recommended to use statistical measures as seen in the following papers:

-Reliability evaluation of groundwater quality index using data-driven models

-Reliability assessment of water quality index based on guidelines of national sanitation foundation in natural streams: integration of remote sensing and data-driven models

-Riprap incipient motion for overtopping flows with machine learning models

6-Improve Figures 4&5. based on the above-mentioned papers.

7-Authors are recommended to carefully read the above-mentioned papers and re-write the paper based these papers.

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