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A Feature Selection Based on the Farmland Fertility Algorithm for Improved Intrusion Detection Systems

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Abstract

The development and expansion of the Internet and cyberspace have increased computer systems attacks; therefore, Intrusion Detection Systems (IDSs) are needed more than ever. Machine learning algorithms have recently been used as successful IDSs; however, due to the high dimensions in IDSs, Feature Selection (FS) plays an essential role in these systems' performance. In this paper, a binary version of the Farmland Fertility Algorithm (FFA) called BFFA is presented to FS in the classification of IDSs. In the proposed method, the V-shaped function is used to move the FFA processes in the binary space, as a result of which the V-shaped function changes the continuous position of the solutions in the FFA algorithm to binary mode. A hybrid approach to classifiers and the BFFA is presented as a fast and robust IDS. The proposed method is tested on two valid IDSs datasets, namely NSL-KDD and UNSW-NB15, and is compared in Accuracy, Precision, Recall, and F1_Score criteria with K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Adaboost (ADA_BOOST), and Naive Bayes (NB) classifiers. The simulation results showed that the proposed method performed better than the classifiers in Accuracy, Precision, and Recall criteria; moreover, the proposed method has a better run time in the FS operation.

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Authors and Affiliations

  1. Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran

    Touraj Sattari Naseri & Farhad Soleimanian Gharehchopogh

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  1. Touraj Sattari Naseri
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  2. Farhad Soleimanian Gharehchopogh
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Correspondence to Farhad Soleimanian Gharehchopogh.

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Naseri, T.S., Gharehchopogh, F.S. A Feature Selection Based on the Farmland Fertility Algorithm for Improved Intrusion Detection Systems. J Netw Syst Manage 30, 40 (2022). https://doi.org/10.1007/s10922-022-09653-9

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  • DOI: https://doi.org/10.1007/s10922-022-09653-9

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