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Symptoms-Based Network Intrusion Detection System

Anomaly detection; Classification (of information); Computer crime; Engines; Intrusion detection; Network security; Anomaly; Centralised; Cyber-attacks; Defence mechanisms; Detection agents; Feature; Intrusion Detection Systems; Malicious activities; Network intrusion detection systems; Signature; M...

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Bibliographic Details
Main Authors:	Qassim Q.S., Jamil N., Mahdi M.N.
Other Authors:	36613541700
Format:	Conference Paper
Published:	Springer Science and Business Media Deutschland GmbH 2023
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id	my.uniten.dspace-26447
record_format	dspace
spelling	my.uniten.dspace-264472023-05-29T17:10:38Z Symptoms-Based Network Intrusion Detection System Qassim Q.S. Jamil N. Mahdi M.N. 36613541700 36682671900 56727803900 Anomaly detection; Classification (of information); Computer crime; Engines; Intrusion detection; Network security; Anomaly; Centralised; Cyber-attacks; Defence mechanisms; Detection agents; Feature; Intrusion Detection Systems; Malicious activities; Network intrusion detection systems; Signature; Machine learning Protecting the network perimeters from malicious activities is a necessity and essential defence mechanism against cyberattacks. Network Intrusion Detection system (NIDS) is commonly used as a defense mechanism. This paper presents the Symptoms-based NIDS, a new intrusion detection system approach that learns the normal network behaviours through monitoring a range of network data attributes at the network and the transport layers. The proposed IDS consists of distributed anomaly detection agents and a centralised anomaly classification engine. The detection agents are located at the end nodes of the protected network, detecting anomalies by analysing network traffic and identifying abnormal activities. These agents will capture and analyse the network and the transport headers of individual packets for malicious activities. The agents will communicate with the centralised anomaly classification engine upon detecting a suspicious activity for attack prioritisation and classification. The paper presented a list of network attributes to be considered as classification features to identify anomalies. � 2021, Springer Nature Switzerland AG. Final 2023-05-29T09:10:38Z 2023-05-29T09:10:38Z 2021 Conference Paper 10.1007/978-3-030-90235-3_42 2-s2.0-85120533415 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120533415&doi=10.1007%2f978-3-030-90235-3_42&partnerID=40&md5=0708c59ddda53931ac2899890a5d4482 https://irepository.uniten.edu.my/handle/123456789/26447 13051 LNCS 482 494 Springer Science and Business Media Deutschland GmbH Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
description	Anomaly detection; Classification (of information); Computer crime; Engines; Intrusion detection; Network security; Anomaly; Centralised; Cyber-attacks; Defence mechanisms; Detection agents; Feature; Intrusion Detection Systems; Malicious activities; Network intrusion detection systems; Signature; Machine learning
author2	36613541700
author_facet	36613541700 Qassim Q.S. Jamil N. Mahdi M.N.
format	Conference Paper
author	Qassim Q.S. Jamil N. Mahdi M.N.
spellingShingle	Qassim Q.S. Jamil N. Mahdi M.N. Symptoms-Based Network Intrusion Detection System
author_sort	Qassim Q.S.
title	Symptoms-Based Network Intrusion Detection System
title_short	Symptoms-Based Network Intrusion Detection System
title_full	Symptoms-Based Network Intrusion Detection System
title_fullStr	Symptoms-Based Network Intrusion Detection System
title_full_unstemmed	Symptoms-Based Network Intrusion Detection System
title_sort	symptoms-based network intrusion detection system
publisher	Springer Science and Business Media Deutschland GmbH
publishDate	2023
_version_	1806424500529528832
score	13.211869

Symptoms-Based Network Intrusion Detection System

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