Modelling of intelligent intrusion detection system: making a case for snort

Intrusion Detection System (IDS) is a dynamic network security defense technology that can help to provide realtime detection of internal and external attacks on a computer network and alerting the administration for necessary action. However, the inconsistent nature of networks has resulted in a hi...

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Main Authors:	Olanrewaju, Rashidah Funke, Ku zahir, Ku Nor Afiza, Asnawi, Ani Liza, Sanni, Mistura Laide, Ahmed, Abdulkadir Adekunle
Format:	Conference or Workshop Item
Language:	English English
Published:	Institute of Electrical and Electronics Engineers Inc. 2018
Subjects:	T10.5 Communication of technical information
Online Access:	http://irep.iium.edu.my/61397/ http://irep.iium.edu.my/61397/ http://irep.iium.edu.my/61397/ http://irep.iium.edu.my/61397/1/61397_Modelling%20of%20Intelligent%20Intrusion%20Detection%20System_conference%20article.pdf http://irep.iium.edu.my/61397/2/61397_Modelling%20of%20Intelligent%20Intrusion%20Detection%20System_scopus.pdf

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recordtype	eprints
spelling	iium-613972018-10-18T01:41:00Z http://irep.iium.edu.my/61397/ Modelling of intelligent intrusion detection system: making a case for snort Olanrewaju, Rashidah Funke Ku zahir, Ku Nor Afiza Asnawi, Ani Liza Sanni, Mistura Laide Ahmed, Abdulkadir Adekunle T10.5 Communication of technical information Intrusion Detection System (IDS) is a dynamic network security defense technology that can help to provide realtime detection of internal and external attacks on a computer network and alerting the administration for necessary action. However, the inconsistent nature of networks has resulted in a high number of false positives which makes many network administrators thought IDS to be unreliable for today’s network security system. Nowadays, hackers and attackers have created many new viruses and malware to invade one’s computer network system. Hence, this study proposes a method for early detection of an intrusion by using Snort software. The data collected was used to train the Multilayer Feedforward Neural Network (MLFNN) with Back-propagation (BP) algorithm. This MLFNN with BP algorithm was simulated using MATLAB software. The performance of this classifier was evaluated based on three parameters: accuracy, sensitivity, and False Positive Rate (FPR). Preprocessing was done to classify the output data into normal and attack. Performance evaluation was done using confusion matrix on the data. The results showed that network-based intrusion detection system could be employed for early detection of intrusion due to the excellent performance recorded which were 94.92% of accuracy, 97.97% for sensitivity, and 0.69% for FPR Institute of Electrical and Electronics Engineers Inc. 2018-01-22 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/61397/1/61397_Modelling%20of%20Intelligent%20Intrusion%20Detection%20System_conference%20article.pdf application/pdf en http://irep.iium.edu.my/61397/2/61397_Modelling%20of%20Intelligent%20Intrusion%20Detection%20System_scopus.pdf Olanrewaju, Rashidah Funke and Ku zahir, Ku Nor Afiza and Asnawi, Ani Liza and Sanni, Mistura Laide and Ahmed, Abdulkadir Adekunle (2018) Modelling of intelligent intrusion detection system: making a case for snort. In: IEEE Conference on Wireless Sensors, ICWiSe 2017, 13 - 14 November 2017, Riverside Majestic Hotel Kuching. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8267152 10.1109/ICWISE.2017.8267152
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	T10.5 Communication of technical information
spellingShingle	T10.5 Communication of technical information Olanrewaju, Rashidah Funke Ku zahir, Ku Nor Afiza Asnawi, Ani Liza Sanni, Mistura Laide Ahmed, Abdulkadir Adekunle Modelling of intelligent intrusion detection system: making a case for snort
description	Intrusion Detection System (IDS) is a dynamic network security defense technology that can help to provide realtime detection of internal and external attacks on a computer network and alerting the administration for necessary action. However, the inconsistent nature of networks has resulted in a high number of false positives which makes many network administrators thought IDS to be unreliable for today’s network security system. Nowadays, hackers and attackers have created many new viruses and malware to invade one’s computer network system. Hence, this study proposes a method for early detection of an intrusion by using Snort software. The data collected was used to train the Multilayer Feedforward Neural Network (MLFNN) with Back-propagation (BP) algorithm. This MLFNN with BP algorithm was simulated using MATLAB software. The performance of this classifier was evaluated based on three parameters: accuracy, sensitivity, and False Positive Rate (FPR). Preprocessing was done to classify the output data into normal and attack. Performance evaluation was done using confusion matrix on the data. The results showed that network-based intrusion detection system could be employed for early detection of intrusion due to the excellent performance recorded which were 94.92% of accuracy, 97.97% for sensitivity, and 0.69% for FPR
format	Conference or Workshop Item
author	Olanrewaju, Rashidah Funke Ku zahir, Ku Nor Afiza Asnawi, Ani Liza Sanni, Mistura Laide Ahmed, Abdulkadir Adekunle
author_facet	Olanrewaju, Rashidah Funke Ku zahir, Ku Nor Afiza Asnawi, Ani Liza Sanni, Mistura Laide Ahmed, Abdulkadir Adekunle
author_sort	Olanrewaju, Rashidah Funke
title	Modelling of intelligent intrusion detection system: making a case for snort
title_short	Modelling of intelligent intrusion detection system: making a case for snort
title_full	Modelling of intelligent intrusion detection system: making a case for snort
title_fullStr	Modelling of intelligent intrusion detection system: making a case for snort
title_full_unstemmed	Modelling of intelligent intrusion detection system: making a case for snort
title_sort	modelling of intelligent intrusion detection system: making a case for snort
publisher	Institute of Electrical and Electronics Engineers Inc.
publishDate	2018
url	http://irep.iium.edu.my/61397/ http://irep.iium.edu.my/61397/ http://irep.iium.edu.my/61397/ http://irep.iium.edu.my/61397/1/61397_Modelling%20of%20Intelligent%20Intrusion%20Detection%20System_conference%20article.pdf http://irep.iium.edu.my/61397/2/61397_Modelling%20of%20Intelligent%20Intrusion%20Detection%20System_scopus.pdf
first_indexed	2023-09-18T21:27:05Z
last_indexed	2023-09-18T21:27:05Z
_version_	1777412264024866816

Modelling of intelligent intrusion detection system: making a case for snort

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