Feature Selection with IG-R for Improving Performance of Intrusion Detection System

Abstract

As the popularity of the internet computer continued to grow and become an indispensable in human life, the security of computer network has become an important issue in computer security field. The Intrusion Detection System (IDS) is a system used in computer security for network security. The feature selection stage of IDS is considered to be the most critical stage in IDS. This stage is very costly both in efforts and time. However, many machine learning approaches have been presented to improve this stage in order to improve the performance of an IDS. However, these approaches did not give desirable results with respect to the detection accuracy in the IDS. A novel technique is proposed in this paper combining the Information Gain and Ranker (IG+R) method as the feature selection strategy with Naïve Bayes (NB), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) as the classifiers. The performance of these IG+R-NB, IG+R-SVM, and IG+R-KNN was evaluated on NSLKDD dataset. The experimental results of our proposed method gave high accuracy and low false alarm rate. The results obtained was compared and benchmarked with existing works. The results of this paper outperformed the existing approaches in terms of the detection accuracy.

Harris-Hawk-Optimization-Based Deep Recurrent Neural Network for Securing the Internet of Medical Things

“…To achieve effective learning, feature selection (FS) is a technique for selecting and removing a subset of essential attributes from a large amount of redundant and unnecessary information [52]. Feature selection is an approach for deleting unrelated and unnecessary features to improve the training result in detecting learning performance and model project duration [53]. Feature selection can help remove some computations in contrast to replica complexity [54].…”

Comparison of Gain Ratio and Chi-Square Feature Selection Methods in Improving SVM Performance on IDS

“…This phase entails choosing a subset of pertinent features from a broader set of available features. Some examples of research that apply this technique include the application of IG-R to improve IDS performance [11], research that uses a combination of PSO and CFS for selecting features [12], and the Farmland Fertility Algorithm [13]. The importance of feature selection can be seen from previous studies that prove that optimization algorithms can help increase SVM accuracy by up to 36.2% compared to the SVM process without using feature selection optimization [14].…”