Network traffic analytics utilises data collected from network nodes to generate network traffic reports based on real-time updates. These reports can be further analysed to identify meaningful and actionable network traffic patterns using machine learning models.
The collected data is primarily used for 2 routines:
Network traffic analytics can be used in monitoring excessive network traffic, odd DNS communications, large internet bound uploads, too long connections, etc. It also uses machine learning for creating profiles of normal behaviour which is then used to detect pattern deviations or any anomalous behaviour.
Incidents can be in various forms including unauthorised logs or data breaches. The logs of such incidents are essential for proactive incident response as it provides complete historical visibility on previous incident patterns and corresponding effective remediation taken to mitigate the risks. The logs and flows are integrated with meta data which reduces Mean Time to Know (MTTK) and provides more context about an incident.
Benefits of Network Traffic Analytics
- Network traffic analytics is used in detecting anomalies and security threats from internal and external sources.
- Network traffic analytics provides a real-time analysis for accurate detection of malicious behaviour in network and to protect sensitive data.
- It provides complete visibility across networks to observe network traffic and make decisions accordingly.
- Network traffic analytics helps in solving network issues faster by finding out most used applications which are generating traffic.
- Network traffic analytics helps in following weekly, monthly traffic trends to know about bandwidth capacity and provide details about which protocols, application, interface and users are consuming maximum bandwidth.
Network traffic analytics reduces the Mean Time to Know about an incident by continuously monitoring network traffic to detect abnormal behaviours. By correlating logs, flows and meta data, network traffic analytics provides a background about an incident and can help in identifying network incidents such as distributed denial of service (DDoS) attacks and other malicious software intrusions.
– Sonam Chawla