User behavior analytics (UBA) is a cybersecurity process around detecting threats, attacks, and fraud. These solutions watch the patterns of human behaviour and apply different statistical analysis to detect abnormalities from patterns which may indicate threats. User Behavior Analytics is closer to Security and Information Event Management. Security and Information Event Management focus on analyzing events captured in firewalls, OS, and other system logs through pre-defined rules.
Security solutions are becoming a critical component of an enterprise security solution as more companies are now migrating their infrastructure and applications to the cloud. User behaviour analytics (UBA) analyzes activities in individual, business-critical cloud applications. The activities can span multiple applications. With these solutions, enterprises can gain a comprehensive view of user behaviour and activities across their entire enterprise cloud environment from one single user interface. Instead of tracking devices or security events, User Behavior Analytics tracks a system’s users. Apache Hadoop helps in User Behavior Analytics by analyzing petabytes of data to detect advanced persistent threats. Viruses and malware are often identified by their unique signatures. Some attacks may be identified by communication signatures. Modern attacks can evade traditional security measures. Hackers involved in the big retail security breaches are good at stealth entering and leaving without keeping many traces.
While machine learning and other related automation tools are increasingly being used to fight cyber attacks, hackers also growing more sophisticated tools. Many of these solutions are delivered as a cloud service to address the visibility and security challenges which the enterprises usually face in cloud service environments such as AWS, Salesforce, MS Office 365, Google Apps etc. To improve the security for the cloud services, enterprises are implementing these security solutions rather than just focusing on quickly identifying attack objects and continuously monitoring for deviations from the acceptable norm.
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