Publication:
Symptoms-Based Network Intrusion Detection System

Simple item page
dc.contributor.authorQassim Q.S.en_US
dc.contributor.authorJamil N.en_US
dc.contributor.authorMahdi M.N.en_US
dc.contributor.authorid36613541700en_US
dc.contributor.authorid36682671900en_US
dc.contributor.authorid56727803900en_US
dc.date.accessioned2023-05-29T09:10:38Z
dc.date.available2023-05-29T09:10:38Z
dc.date.issued2021
dc.descriptionAnomaly 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 learningen_US
dc.description.abstractProtecting 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.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/978-3-030-90235-3_42
dc.identifier.epage494
dc.identifier.scopus2-s2.0-85120533415
dc.identifier.spage482
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85120533415&doi=10.1007%2f978-3-030-90235-3_42&partnerID=40&md5=0708c59ddda53931ac2899890a5d4482
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26447
dc.identifier.volume13051 LNCS
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.titleSymptoms-Based Network Intrusion Detection Systemen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
Files
Collections
SCOPUS