research-article

An Intrusion Detection Feature Selection Method Based on Improved Fireworks Algorithm

Authors:

Shuangyue Niu,

Xiang Ji,

Jingmei Li,

Di Xue,

Weifei WuAuthors Info & Claims

ICCAI '20: Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence

Pages 356 - 361

https://doi.org/10.1145/3404555.3404556

Published: 20 August 2020 Publication History

Get Access

Abstract

With the rapid development of network technology, network intrusion has become increasingly frequent. In network intrusion detection technology, how to reduce feature dimensions and reduce redundant information is the key to improve the detection accuracy. To solve this problem, this paper proposes a new feature selection method SIFWA for intrusion detection based on improved fireworks algorithm. SIFWA optimized and improved the selection strategy of fireworks algorithm, which adopted the selection strategy based on fitness value to screen the next generation of fireworks, which could greatly improve the ability of fireworks algorithm to find the optimal solution and search efficiency to select more effective features for intrusion detection. Simulation experiments were conducted using UCI data. Simulation results show that SIFWA has higher detection accuracy than the benchmark algorithm.

References

[1]

Yang hongyu, zhao mingrui, xie lixia. Intrusion detection based on adaptive evolutionary neural network algorithm [J]. Computer engineering and science, 2014, 36(08):1469--1475.

Google Scholar

[2]

S Singh, S Silakari. An ensemble approach for feature selection of Cyber Attack Dataset. Computer Science, 2009, 297--302

Google Scholar

[3]

Tan ying, zheng shaoqiu. Research progress of fireworks algorithm [J]. Journal of intelligent systems, 2014, 9(05):515--528.

Google Scholar

[4]

Li lanting. Research on task scheduling strategy based on improved fireworks algorithm [D]. Harbin engineering university, 2019.

Google Scholar

[5]

Tan Boping, Zhou Xianwei, Yang jun et al. Comprehensive evaluation of intrusion detection system based on fuzzy method.Computer engineering, 2006, 32(8): 155--156.

Google Scholar

[6]

Chen tao, xie yangqun. Review of feature dimensionality reduction methods in text classification [J]. Acta sinica sinica, 2005, 24(6):690--695.

Google Scholar

[7]

Wang feng. Application research of ant colony algorithm in network intrusion feature selection [D]. Hunan university, 2017.

Google Scholar

[8]

Gadat S Younes L. A Stochastic Algorithm for Feature Selection in Pattern Recognition. Journal of Machine Learning Research, 2007, 8(3):509--547.

Digital Library

Google Scholar

[9]

Zhang yudong, huo yuankai, wu lenan, dong zhengchao. Review of dimension reduction technology and methods [J]. Journal of sichuan military engineering, 2010, 31(10):1--7.

Google Scholar

[10]

Li min, kamili muyding. Research on feature selection methods and algorithms [J]. Computer technology and development, 2013, 23(12):16--21.

Google Scholar

[11]

zhang youpeng, yan chenyang.A pheromone update strategy for ant colony algorithm based on Metropolis sampling criterion.Railway journal, 2008, 30(3): 114--118.

Google Scholar

[12]

Huang xin, mo haimiao, zhao zhigang, zeng min. Study on discrete enhanced fireworks algorithm and kNN in feature selection [J/OL]. Computer engineering and application:1--8[2019-10-04].

Google Scholar

[13]

Li he. Intrusion detection based on improved fireworks algorithm and SVM [D]. Harbin engineering university, 2019.

Google Scholar

Cited By

View all

Jie XGaofeng LJun J(2021)Research on Solving Method of WTA Problem based on Firework Algorithm2021 4th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)10.1109/AEMCSE51986.2021.00091(406-411)Online publication date: Mar-2021
https://doi.org/10.1109/AEMCSE51986.2021.00091

Index Terms

An Intrusion Detection Feature Selection Method Based on Improved Fireworks Algorithm
1. Security and privacy
  1. Systems security
    1. Operating systems security
      1. Virtualization and security

Recommendations

A Two-step Feature Selection Algorithm Adapting to Intrusion Detection
JCAI '09: Proceedings of the 2009 International Joint Conference on Artificial Intelligence

In intrusion detection data set is high dimensional, which leads to low processing speed for intrusion detection algorithms, but it holds many features affecting little for detection. To address the above issue, a two-step feature selection algorithm is ...
An effective genetic algorithm-based feature selection method for intrusion detection systems
Abstract
Availability of suitable and validated data is a key issue in multiple domains for implementing machine learning methods. Higher data dimensionality has adverse effects on the learning algorithm's performance. This work aims to design a method ...
A comparison of feature-selection methods for intrusion detection
MMM-ACNS'10: Proceedings of the 5th international conference on Mathematical methods, models and architectures for computer network security

Feature selection is an important pre-processing step in intrusion detection. Achieving reduction of the number of relevant traffic features without negative effect on classification accuracy is a goal that greatly improves overall effectiveness of an ...

Comments

Information & Contributors

Information

Published In

ICCAI '20: Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence

April 2020

563 pages

ISBN:9781450377089

DOI:10.1145/3404555

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

University of Tsukuba: University of Tsukuba

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 August 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

ICCAI '20

ICCAI '20: 2020 6th International Conference on Computing and Artificial Intelligence

April 23 - 26, 2020

Tianjin, China

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
37
Total Downloads

Downloads (Last 12 months)5
Downloads (Last 6 weeks)2

Reflects downloads up to 01 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Jie XGaofeng LJun J(2021)Research on Solving Method of WTA Problem based on Firework Algorithm2021 4th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)10.1109/AEMCSE51986.2021.00091(406-411)Online publication date: Mar-2021
https://doi.org/10.1109/AEMCSE51986.2021.00091

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

A Two-step Feature Selection Algorithm Adapting to Intrusion Detection

An effective genetic algorithm-based feature selection method for intrusion detection systems

A comparison of feature-selection methods for intrusion detection

Comments

Information

Published In

In-Cooperation

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations