Feature Subset Selection Using Binary Gravitational Search Algorithm for Intrusion Detection System

被引：0

作者：

Behjat, Amir Rajabi ^{[1
]}

Mustapha, Aida ^{[1
]}

Nezamabadi-pour, Hossein ^{[2
]}

Sulaiman, Md. Nasir ^{[1
]}

Mustapha, Norwati ^{[1
]}

机构：

[1] Univ Putra Malaysia, Fac Comp Sci & Informat Technol, Upm Serdang 43400, Selangor, Malaysia

[2] Shahid Bahonar Univ Kerman, Dept Elect Engn, Kerman, Iran

来源：

INTELLIGENT INFORMATION AND DATABASE SYSTEMS (ACIIDS 2013), PT II | 2013年 / 7803卷

关键词：

Intrusion Detection System; KDD; 99; dataset; GSA; Feature Selection; MLP;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Due to control different infrastructures of networked computers in cyber security, intrusion detection system has been an important task essentially. Today, an effective intrusion detection system utilizes computational methods as machine learning techniques to improve detection rate with lowest false positive rate; however large number of irrelevant features as an optimization problem decrease this rate. This study using Binary Search Gravitational Algorithm (BGSA) as a feature selection method decreases irrelevant features in KDD 99 intrusion detection data set in order to improve Multi-layer perceptron performance. Results show that significant and relevant features increase performance of intrusion detection system near to 100% with lowest computational cost.

引用

页码：377 / 386

页数：10

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