No / Technological 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.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/19793 |
| Date | 19 December 2023 |
| Creators | Lefoane, Moemedi, Ghafir, Ibrahim, Kabir, Sohag, Awan, Irfan U. |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Conference paper, No full-text in the repository |
| Rights | Unspecified |
| Relation | https://icfnds.org/ |
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