A Hybrid Intrusion Detection System Based on Multilayer Artificial Neural Network and Intelligent Feature Selection

Mehdi MANSOURI; Mohadese TORABI GOLSEFID; Naser NEMATBAKHSH

Article Details

A Hybrid Intrusion Detection System Based on Multilayer Artificial Neural Network and Intelligent Feature Selection

Authors:

Mehdi MANSOURI | Mohadese TORABI GOLSEFID | Naser NEMATBAKHSH

Publisher:

Sivas Cumhuriyet Üniversitesi

Journal:

Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi 2015 Vol.36 Issue 3, pp.2686-2692

Document Type:

Article

Publication Date:

5/2015

Document Language:

Turkish

Document ID (Org No.):

2000921718

Frequency:

Quarterly

eISSN:

2587-246X

pISSN:

2587-2680

Abstract:

 


Abstract. Increased intrusions into computer networks and cyber-attacks have rendered the immunization of cyberspace one of the most important issues of managers and experts in the recent years. Since cyber-attacks have become more sophisticated and hackers have become more professional, mere use techniques such as firewall, cryptography, biometrics, and antiviruses is not sufficient anymore. Therefore, it is necessary to employ efficient intrusion detection systems. Considering 5 classes of cyber-attacks, a detection intrusion system, of abuse detection type, based on the combination of a multilayer artificial neural network and an intelligent feature selection method was introduced in this research. The research results indicated that the feature selection phase using the proposed method yielded more favorable outcomes than the compared method in terms of the evaluation criteria.

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Article Details

A Hybrid Intrusion Detection System Based on Multilayer Artificial Neural Network and Intelligent Feature Selection

Authors: Mehdi MANSOURI | Mohadese TORABI GOLSEFID | Naser NEMATBAKHSH

Publisher: Sivas Cumhuriyet Üniversitesi

Journal: Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi 2015 Vol.36 Issue 3, pp.2686-2692

Document Type: Article

Publication Date: 5/2015

Document Language: Turkish

Document ID (Org No.): 2000921718

Frequency: Quarterly

eISSN: 2587-246X

pISSN: 2587-2680

Issues

Authors:

Mehdi MANSOURI | Mohadese TORABI GOLSEFID | Naser NEMATBAKHSH

Publisher:

Sivas Cumhuriyet Üniversitesi

Journal:

Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi 2015 Vol.36 Issue 3, pp.2686-2692

Document Type:

Article

Publication Date:

5/2015

Document Language:

Turkish

Document ID (Org No.):

2000921718

Frequency:

Quarterly

eISSN:

2587-246X

pISSN:

2587-2680