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dc.contributor.authorLefoane, Moemedi
dc.contributor.authorGhafir, Ibrahim
dc.contributor.authorKabir, Sohag
dc.contributor.authorAwan, Irfan U.
dc.date.accessioned2023-12-19T16:59:42Z
dc.date.accessioned2024-02-02T16:27:18Z
dc.date.available2023-12-19T16:59:42Z
dc.date.available2024-02-02T16:27:18Z
dc.date.issued2023-12
dc.identifier.citationLefoane M, Ghafir I, Kabir S et al (2023) Sequential Pattern Mining: A Proposed Approach for Intrusion Detection Systems. The 7th International Conference on Future Networks & Distributed Systems. Dec 21-22, Dubai, United Arab Emirates.en_US
dc.identifier.urihttp://hdl.handle.net/10454/19793
dc.descriptionNoen_US
dc.description.abstractTechnological advancements have played a pivotal role in the rapid proliferation of the fourth industrial revolution (4IR) through the deployment of Internet of Things (IoT) devices in large numbers. COVID-19 caused serious disruptions across many industries with lockdowns and travel restrictions imposed across the globe. As a result, conducting business as usual became increasingly untenable, necessitating the adoption of new approaches in the workplace. For instance, virtual doctor consultations, remote learning, and virtual private network (VPN) connections for employees working from home became more prevalent. This paradigm shift has brought about positive benefits, however, it has also increased the attack vectors and surfaces, creating lucrative opportunities for cyberattacks. Consequently, more sophisticated attacks have emerged, including the Distributed Denial of Service (DDoS) and Ransomware attacks, which pose a serious threat to businesses and organisations worldwide. This paper proposes a system for detecting malicious activities in network traffic using sequential pattern mining (SPM) techniques. The proposed approach utilises SPM as an unsupervised learning technique to extract intrinsic communication patterns from network traffic, enabling the discovery of rules for detecting malicious activities and generating security alerts accordingly. By leveraging this approach, businesses and organisations can enhance the security of their networks, detect malicious activities including emerging ones, and thus respond proactively to potential threats.en_US
dc.language.isoenen_US
dc.subjectScanning detectionen_US
dc.subjectSequential pattern miningen_US
dc.subjectUnsupervised learningen_US
dc.subjectIntrusion detection systemen_US
dc.subjectNetwork securityen_US
dc.titleSequential Pattern Mining: A Proposed Approach for Intrusion Detection Systemsen_US
dc.status.refereedYesen_US
dc.date.Accepted2023-12-09
dc.typeConference paperen_US
dc.type.versionNo full-text in the repositoryen_US
dc.rights.licenseUnspecifieden_US
dc.date.updated2023-12-19T16:59:43Z
refterms.dateFOA2024-02-02T16:28:55Z
dc.relation.urlhttps://icfnds.org/
dc.openaccess.statusclosedAccessen_US


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