With the increasing volume and complexity of network traffic in social
networks, extracting meaningful insights from this data has become increasingly
challenging. This paper presents a lightweight approach for analysing network traffic
for social networks that enables the identification of patterns and anomalies that may
indicate malicious activity. The paper starts by discussing the importance of network
traffic visualisation and the challenges associated with it. It then provides an overview
of the key components of network traffic data and various visualisation techniques that
can be used to gain insights into network behaviour. The focus is on lightweight
visualisation techniques that can be used to analyse network packet data for threat
detection. Time series plots, scatter plots, heatmaps, and network graphs are some of
the visualisation techniques that can be used to identify patterns and anomalies in
network traffic for social networks. The lightweight nature of this approach enables
efficient processing and analysis of large and complex datasets. In conclusion,
analysing and visualizing network packet data is a crucial technique for identifying
potential security threats, and a lightweight approach can enable efficient processing
and analysis of large and complex network traffic of social networks. By using the
techniques and tools presented in this paper, network administrators and researchers
can gain valuable insights into network behaviour and identify potential security
threats.
Keywords: Analysis, Network, Network traffic, Social network, Threat hunting, Visualisation.
