Network traffic monitoring for SME's
Network Traffic monitoring can be a solution that an SME can implement within their organization to have a better view of the network and activities. The paper proposes three approaches to how we can use Artificial Intelligence and Machine Learning Trends in NTM applications.
Manzoor Ahsan, 2021
Bachelor Thesis, Institute for Information Systems, School of Business FHNW
Betreuende Dozierende: Christopher Scherb
Keywords: network traffic monitoring and analysis
Many companies, especially small companies, do not have an overview of what their typical network traffic looks like so they are also not able to detect anomalies. First, we define what is Network traffic monitoring and then propose methods to automate the monitoring process.
In this thesis, the student should analyze (based on literature) options for small companies to monitor their network traffic without having an incident response team in the company, how anomalies in the network traffic can be found and how monitoring data can be used in response to a breach.
Cybersecurity today is an essential part of any business. SMEs in this regard are lacking behind and need affordable solutions to protect their company’s sensitive data. Network Traffic monitoring can be one of the solutions that an SME can implement within their organization to have a better view of the network and activities over the network. Different methods are used for NTM and there are many tools available today for network monitoring. The current trends of ML, AI, and deep learning are discussed in this paper and the paper proposes three approaches to how we can use these current trends in NTM applications. The principal goal of this paper is to give a better understanding of NTM technologies.
Studiengang: Business Information Technology (Bachelor)
Fachbereich der Arbeit: Business Information System & IT-Management