Snort-based smart and swift intrusion detection system

In this paper, a smart Intrusion Detection System (IDS) has been proposed that detects network attacks in less time after monitoring incoming traffic thus maintaining better performance. Methods/Statistical Analysis: The features are extracted using back-propagation algorithm. Then, only these relev...

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Main Authors:	Olanrewaju, Rashidah Funke, Khan, Burhan Ul Islam, Najeeb, Athaur Rahman, Ku zahir, Ku Nor Afiza, Hussain, Sabahat
Format:	Article
Language:	English
Published:	Informatics (India) Limited 2018
Subjects:	QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://irep.iium.edu.my/62513/ http://irep.iium.edu.my/62513/ http://irep.iium.edu.my/62513/ http://irep.iium.edu.my/62513/2/Snort-Based%20Smart%20and%20Swift%20Intrusion%20Detection.pdf

id	iium-62513
recordtype	eprints
spelling	iium-625132018-03-28T03:06:14Z http://irep.iium.edu.my/62513/ Snort-based smart and swift intrusion detection system Olanrewaju, Rashidah Funke Khan, Burhan Ul Islam Najeeb, Athaur Rahman Ku zahir, Ku Nor Afiza Hussain, Sabahat QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering In this paper, a smart Intrusion Detection System (IDS) has been proposed that detects network attacks in less time after monitoring incoming traffic thus maintaining better performance. Methods/Statistical Analysis: The features are extracted using back-propagation algorithm. Then, only these relevant features are trained with the help of multi-layer perceptron supervised neural network. The simulation is performed using MATLAB. Findings: The proposed system has been verified to have high accuracy rate, high sensitivity as well as a reduction in false positive rate. Besides, the intrusions have been classified into four categories as Denial-of-Service (DoS), User-to-root (U2R), Remote-to-Local (R2L) and Probe attacks; and the alerts are stored and shared via a central log. Thus, the unknown attacks detected by other Intrusion Detection Systems can be sensed by any IDS in the network thereby reducing computational cost as well as enhancing the overall detection rate. Applications/Improvements: The proposed system does not waste time by considering and analysing all the features but takes into consideration only relevant ones for the specific attack and supervised Informatics (India) Limited 2018-01 Article PeerReviewed application/pdf en http://irep.iium.edu.my/62513/2/Snort-Based%20Smart%20and%20Swift%20Intrusion%20Detection.pdf Olanrewaju, Rashidah Funke and Khan, Burhan Ul Islam and Najeeb, Athaur Rahman and Ku zahir, Ku Nor Afiza and Hussain, Sabahat (2018) Snort-based smart and swift intrusion detection system. Indian Journal of Science and Technology, 11 (4). pp. 1-9. ISSN 0974-6846 E-ISSN 0974-5645 http://indjst.org/index.php/indjst/article/view/120917/83466 10.17485/ijst/2018/v11i4/120917
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering Olanrewaju, Rashidah Funke Khan, Burhan Ul Islam Najeeb, Athaur Rahman Ku zahir, Ku Nor Afiza Hussain, Sabahat Snort-based smart and swift intrusion detection system
description	In this paper, a smart Intrusion Detection System (IDS) has been proposed that detects network attacks in less time after monitoring incoming traffic thus maintaining better performance. Methods/Statistical Analysis: The features are extracted using back-propagation algorithm. Then, only these relevant features are trained with the help of multi-layer perceptron supervised neural network. The simulation is performed using MATLAB. Findings: The proposed system has been verified to have high accuracy rate, high sensitivity as well as a reduction in false positive rate. Besides, the intrusions have been classified into four categories as Denial-of-Service (DoS), User-to-root (U2R), Remote-to-Local (R2L) and Probe attacks; and the alerts are stored and shared via a central log. Thus, the unknown attacks detected by other Intrusion Detection Systems can be sensed by any IDS in the network thereby reducing computational cost as well as enhancing the overall detection rate. Applications/Improvements: The proposed system does not waste time by considering and analysing all the features but takes into consideration only relevant ones for the specific attack and supervised
format	Article
author	Olanrewaju, Rashidah Funke Khan, Burhan Ul Islam Najeeb, Athaur Rahman Ku zahir, Ku Nor Afiza Hussain, Sabahat
author_facet	Olanrewaju, Rashidah Funke Khan, Burhan Ul Islam Najeeb, Athaur Rahman Ku zahir, Ku Nor Afiza Hussain, Sabahat
author_sort	Olanrewaju, Rashidah Funke
title	Snort-based smart and swift intrusion detection system
title_short	Snort-based smart and swift intrusion detection system
title_full	Snort-based smart and swift intrusion detection system
title_fullStr	Snort-based smart and swift intrusion detection system
title_full_unstemmed	Snort-based smart and swift intrusion detection system
title_sort	snort-based smart and swift intrusion detection system
publisher	Informatics (India) Limited
publishDate	2018
url	http://irep.iium.edu.my/62513/ http://irep.iium.edu.my/62513/ http://irep.iium.edu.my/62513/ http://irep.iium.edu.my/62513/2/Snort-Based%20Smart%20and%20Swift%20Intrusion%20Detection.pdf
first_indexed	2023-09-18T21:28:35Z
last_indexed	2023-09-18T21:28:35Z
_version_	1777412358282412032

Snort-based smart and swift intrusion detection system

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