عنوان المقالة:Improving Intrusion Detection System by Developing Feature Selection Model Based on Firefly Algorithm and Support Vector Machine Improving Intrusion Detection System by Developing Feature Selection Model Based on Firefly Algorithm and Support Vector Machine
The nowadays growing of threads and intrusions
on networks make the need for developing efficient and effective
intrusion detection systems a necessity. Powerful solutions of
intrusion detection systems should be capable of dealing with
central network issues such as huge data, high-speed traffic, and
wide variety in threat types. This paper proposes a wrapper
feature selection method that is based on firefly algorithm and
support vector machine. The firefly optimization algorithm has
been effectively employed in diverse combinatorial problems.
The proposed method improves the performance of intrusion
detection by removing the irrelevant features and reduces the
time of classification by reducing the dimension of data. The
SVM model was employed to evaluate each of the feature
subsets produced from firefly technique. The main merit of
the proposed method is its ability in modifying the firefly
algorithm to become suitable for selection of features. To
validate the proposed approach, the popular NSL-KDD dataset
was used in addition to the common measures of intrusion
detection systems such as overall accuracy, detection rate, and
false alarm rate. The proposed method achieved an overall
accuracy of 78.89% compared with 75.81% for all the 41
features. The analysis results approved the effectiveness of
the proposed feature selection method in enhancing network
intrusion detection system.
Abstract
The nowadays growing of threads and intrusions
on networks make the need for developing efficient and effective
intrusion detection systems a necessity. Powerful solutions of
intrusion detection systems should be capable of dealing with
central network issues such as huge data, high-speed traffic, and
wide variety in threat types. This paper proposes a wrapper
feature selection method that is based on firefly algorithm and
support vector machine. The firefly optimization algorithm has
been effectively employed in diverse combinatorial problems.
The proposed method improves the performance of intrusion
detection by removing the irrelevant features and reduces the
time of classification by reducing the dimension of data. The
SVM model was employed to evaluate each of the feature
subsets produced from firefly technique. The main merit of
the proposed method is its ability in modifying the firefly
algorithm to become suitable for selection of features. To
validate the proposed approach, the popular NSL-KDD dataset
was used in addition to the common measures of intrusion
detection systems such as overall accuracy, detection rate, and
false alarm rate. The proposed method achieved an overall
accuracy of 78.89% compared with 75.81% for all the 41
features. The analysis results approved the effectiveness of
the proposed feature selection method in enhancing network
intrusion detection system.