Relevant Feature Selection Model Using Data Mining for Intrusion Detection System

Abstract

Abstract-Network intrusions have become a significant threat in recent years as a result of the increased demand of computer networks for critical systems. Intrusion detection system (IDS) has been widely deployed as a defense measure for computer networks. Features extracted from network traffic can be used as sign to detect anomalies. However with the huge amount of network traffic, collected data contains irrelevant and redundant features that affect the detection rate of the IDS, consumes high amount of system resources, and slowdown the training and testing process of the IDS. In this paper, a new feature selection model is proposed; this model can effectively select the most relevant features for intrusion detection. Our goal is to build a lightweight intrusion detection system by using a reduced features set. Deleting irrelevant and redundant features helps to build a faster training and testing process, to have less resource consumption as well as to maintain high detection rates. The effectiveness and the feasibility of our feature selection model were verified by several experiments on KDD intrusion detection dataset. The experimental results strongly showed that our model is not only able to yield high detection rates but also to speed up the detection process.

Seleksi Fitur Dengan Information Gain Untuk Meningkatkan Deteksi Serangan DDoS menggunakan Random Forest

“…Selain itu, terlalu banyak fitur yang tidak relevan akan menghasilkan kategori kelas yang tidak berhubungan [9]. Mengacu pada penelitian [10]& [11]disebutkan bahwa seleksi fitur dapat meningkatkan akurasi algorithma klasifikasi. Oleh karenanya penelitian ini bertujuan meningkatkan performa deteksi serangan DDoS dengan menggunakan teknik seleksi fitur.…”

Evaluating the Observability of Network Security Monitoring Strategies With TOMATO

“…While intrusion detection remains a popular research domain, less attention has been placed upon the identification of detection sensor placement within a network. Work in [10] explored the use of Bayesian networks to identify sensor placement in a network, while in [11] the authors propose feature selection techniques necessary for IDS [12]. To complement this, researchers have also explored models to analyze and detect the propagation of attacks through a network.…”

A Review on High Ranked Features based NIDS

“…Relevant Feature Selection Model Using Data Mining for Intrusion Detection System [11]: To build a lightweight intrusion detection system, a relevant feature selection model was developed to select the best features set which uses seven different feature evaluation methods to select and rank relevant features. This model has four different stages, Data Pre-Processing, Best Classifier Selection, Feature Reduction, and Best Features Selection.…”