چکیده (انگلیسی):

Network security is a serious global concern. Usefulness Intrusion Detection Systems (IDS) are increasing
incredibly in Information Security research using Soft computing techniques. In the previous researches
having irrelevant and redundant features are recognized causes of increasing the processing speed of
evaluating the known intrusive patterns. In addition, an efficient feature selection method eliminates
dimension of data and reduce redundancy and ambiguity caused by none important attributes. Therefore,
feature selection methods are well-known methods to overcome this problem. There are various approaches
being utilized in intrusion detections, they are able to perform their method and relatively they are achieved
with some improvements. This work is based on the enhancement of the highest Detection Rate (DR)
algorithm which is Linear Genetic Programming (LGP) reducing the False Alarm Rate (FAR) incorporates
with Bees Algorithm. Finally, Support Vector Machine (SVM) is one of the best candidate solutions to
settle IDSs problems. In this study four sample dataset containing 4000 random records are excluded
randomly from this dataset for training and testing purposes. Experimental results show that the LGP_BA
method improves the accuracy and efficiency compared with the previous related research and the feature
subcategory offered by LGP_BA gives a superior representation of data.