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

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.