SABADT: Hybrid Intrusion Detection Approach for Cyber Attacks Identification in WLAN

With the advancement of technology, the use of wireless media and devices are increasing every day. In particular, the use of wireless local area networks (WLAN) has increased rapidly in recent years and is expected to increase further. The current state of wireless local area network technologies m...

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Autores principales:	Merve Ozkan-Okay, Omer Aslan, Recep Eryigit, Refik Samet
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Wireless LAN intrusion detection system hybrid model signature based technique anomaly based technique machine learning Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/5245a22f018842cdab9b8e8d3613b153
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spelling	oai:doaj.org-article:5245a22f018842cdab9b8e8d3613b1532021-12-03T00:01:19ZSABADT: Hybrid Intrusion Detection Approach for Cyber Attacks Identification in WLAN2169-353610.1109/ACCESS.2021.3129600https://doaj.org/article/5245a22f018842cdab9b8e8d3613b1532021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9622260/https://doaj.org/toc/2169-3536With the advancement of technology, the use of wireless media and devices are increasing every day. In particular, the use of wireless local area networks (WLAN) has increased rapidly in recent years and is expected to increase further. The current state of wireless local area network technologies makes the network vulnerable to attacks ranging from passive listening to active intervention. Intrusion detection systems (IDSs) are being developed against these kinds of attacks. The IDSs play an important role in WLAN security by detecting and preventing malicious activities. However, most techniques used in IDSs cannot cope with dynamic and complex attacks. The aim of this study is to reduce the deficiencies in present IDSs for WLANs and build a more effective system which can detect unknown and complex attack variants dynamically. In this context, a methodology has been proposed. The proposed methodology basically has two contributions. The first contribution is the Feature Selection Approach (FSAP) to increase the speed of attack detection by reducing the number of used features. The second contribution is the hybrid attack detection technique, SABADT (Signature and Anomaly Based Attack Detection Technique), which detects attacks fast with high accuracy. The proposed methodology is implemented on the KDD’99 and UNSW-NB15 datasets. The obtained results are compared with existing machine learning techniques. The detection model is created by using KDD’99 and UNSW-NB15 training datasets and tested on the KDD’99 and UNSW-NB15 test datasets. The obtained 99.65% and 99.17% accuracy rates are quite high when compared to leading methods in the literature. In addition, common tools were used to obtain a mix of normal activities and current attack behaviors in order to test on novel attacks within the scope of the study. The different types of attacks were captured with the Wireshark tool. Some of the captured attacks were used only in the testing phase. In this test case, the attacks were detected with an accuracy rate of 99.69%.Merve Ozkan-OkayOmer AslanRecep EryigitRefik SametIEEEarticleWireless LANintrusion detection systemhybrid modelsignature based techniqueanomaly based techniquemachine learningElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 157639-157653 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Wireless LAN intrusion detection system hybrid model signature based technique anomaly based technique machine learning Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Wireless LAN intrusion detection system hybrid model signature based technique anomaly based technique machine learning Electrical engineering. Electronics. Nuclear engineering TK1-9971 Merve Ozkan-Okay Omer Aslan Recep Eryigit Refik Samet SABADT: Hybrid Intrusion Detection Approach for Cyber Attacks Identification in WLAN
description	With the advancement of technology, the use of wireless media and devices are increasing every day. In particular, the use of wireless local area networks (WLAN) has increased rapidly in recent years and is expected to increase further. The current state of wireless local area network technologies makes the network vulnerable to attacks ranging from passive listening to active intervention. Intrusion detection systems (IDSs) are being developed against these kinds of attacks. The IDSs play an important role in WLAN security by detecting and preventing malicious activities. However, most techniques used in IDSs cannot cope with dynamic and complex attacks. The aim of this study is to reduce the deficiencies in present IDSs for WLANs and build a more effective system which can detect unknown and complex attack variants dynamically. In this context, a methodology has been proposed. The proposed methodology basically has two contributions. The first contribution is the Feature Selection Approach (FSAP) to increase the speed of attack detection by reducing the number of used features. The second contribution is the hybrid attack detection technique, SABADT (Signature and Anomaly Based Attack Detection Technique), which detects attacks fast with high accuracy. The proposed methodology is implemented on the KDD’99 and UNSW-NB15 datasets. The obtained results are compared with existing machine learning techniques. The detection model is created by using KDD’99 and UNSW-NB15 training datasets and tested on the KDD’99 and UNSW-NB15 test datasets. The obtained 99.65% and 99.17% accuracy rates are quite high when compared to leading methods in the literature. In addition, common tools were used to obtain a mix of normal activities and current attack behaviors in order to test on novel attacks within the scope of the study. The different types of attacks were captured with the Wireshark tool. Some of the captured attacks were used only in the testing phase. In this test case, the attacks were detected with an accuracy rate of 99.69%.
format	article
author	Merve Ozkan-Okay Omer Aslan Recep Eryigit Refik Samet
author_facet	Merve Ozkan-Okay Omer Aslan Recep Eryigit Refik Samet
author_sort	Merve Ozkan-Okay
title	SABADT: Hybrid Intrusion Detection Approach for Cyber Attacks Identification in WLAN
title_short	SABADT: Hybrid Intrusion Detection Approach for Cyber Attacks Identification in WLAN
title_full	SABADT: Hybrid Intrusion Detection Approach for Cyber Attacks Identification in WLAN
title_fullStr	SABADT: Hybrid Intrusion Detection Approach for Cyber Attacks Identification in WLAN
title_full_unstemmed	SABADT: Hybrid Intrusion Detection Approach for Cyber Attacks Identification in WLAN
title_sort	sabadt: hybrid intrusion detection approach for cyber attacks identification in wlan
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/5245a22f018842cdab9b8e8d3613b153
work_keys_str_mv	AT merveozkanokay sabadthybridintrusiondetectionapproachforcyberattacksidentificationinwlan AT omeraslan sabadthybridintrusiondetectionapproachforcyberattacksidentificationinwlan AT receperyigit sabadthybridintrusiondetectionapproachforcyberattacksidentificationinwlan AT refiksamet sabadthybridintrusiondetectionapproachforcyberattacksidentificationinwlan
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SABADT: Hybrid Intrusion Detection Approach for Cyber Attacks Identification in WLAN

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