Forensic Data Analytics for Anomaly Detection in Evolving Networks

In the prevailing convergence of traditional infrastructure-based deployment (i.e., Telco and industry operational networks) towards evolving deployments enabled by 5G virtualization, there is a keen interest in elaborating effective security controls to protect these in-depth. By considering key enabling technologies like networks are democratized, facilitating establishment point presences integrating different business models ranging from media, dynamic web content, gaming, plethora IoT use cases. Despite increasing services provided networks, many cybercrimes attacks have been launched perform malicious activities. Due limitations artifacts (e.g., firewalls intrusion detection systems), research on digital forensic data analytics has attracted more attention. Digital enables people derive detailed information comprehensive conclusions perspectives assist convicting criminals preventing future crimes. This chapter presents framework for network anomaly detection, including multi-perspective feature engineering, unsupervised result correction procedures. Experiments real-world show effectiveness proposed solution.