GAB-BBO: Adaptive Biogeography Based Feature Selection Approach for Intrusion Detection

DOI

10.2991/ijcis.2017.10.1.61How to use a DOI?

Keywords

NP-hard combinatorial optimization problem; biogeography based optimization; evolutionary state estimation approach; Hamming distance; Feature selection; intrusion detection

Abstract

Feature selection is used as a preprocessing step in the resolution of many problems using machine learning. It aims to improve the classification accuracy, speed up the model generation process, reduce the model complexity and reduce the required storage space. Feature selection is an NP-hard combinatorial optimization problem. It is the process of selecting a subset of relevant, non-redundant features from the original ones. Among the works that are proposed to solve this problem, few are dedicated for intrusion detection. This paper presents a new feature selection approach for intrusion detection, using the Biogeography Based Optimization (BBO) algorithm. The approach which is named Guided Adaptive Binary Biogeography Based Optimization (GAB-BBO) uses the evolutionary state estimation (ESE) approach and a new migration and mutation operators. The ESE approach we propose in this paper uses the Hamming distance between the binary solutions to calculate an evolutionary factor f which determines the population diversity. During this process, fuzzy logic is used through a fuzzy classification method, to perform the transition between the numerical f value and four evolutionary states which are : convergence, exploration, exploitation and jumping out. According to the state identified, GAB-BBO adapts the algorithm behavior using a new adaptive strategy. The performances of GAB-BBO are evaluated on benchmark functions and the Kdd’99 intrusion detection dataset. In addition, we use other different datasets for further validation. Comparative study with other algorithms is performed and the results show the effectiveness of the proposed approach.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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TY  - JOUR
AU  - Wassila Guendouzi
AU  - Abdelmadjid Boukra
PY  - 2017
DA  - 2017/05/01
TI  - GAB-BBO: Adaptive Biogeography Based Feature Selection Approach for Intrusion Detection
JO  - International Journal of Computational Intelligence Systems
SP  - 914
EP  - 935
VL  - 10
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2017.10.1.61
DO  - 10.2991/ijcis.2017.10.1.61
ID  - Guendouzi2017
ER  -