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Detecting Unusual Activities Using SureLog Next Generation SIEM | ANET
Detecting Unusual Activities Using SureLog Next Generation SIEM
Next-Gen SIEMs have brought new capabilities to organizations and their security teams. They’re built from the ground up to take advantage of big data, machine learning, and other cutting-edge technologies. Next-Gen SIEM Platform can help organizations detect and respond to threats faster than ever before. The biggest advantage of Next-Gen SIEMs is security analytics. Security analytics use cases generally fall into three broad categories.
- Real-Time Rule-Based,
- Real-Time Security Analytics,
- Batch Security.
Real-Time Rule-Based Use Cases
Usually, real-time rule-based use cases apply to the detection and remediation of known cyber-attacks or attackers; specifically, rule-based analytics draws from threat intelligence feeds. Additionally, real-time rule-based use cases define and detect rule-based approaches such as SureLog SIEM.
Real-Time Security Analytics Use Cases
Real-time means operating on data as it flows through a set of analytics. It means being able to interact with, analyze, augment and visualize that data in milliseconds to seconds of response time.
Moreover, it can match potential threat patterns requiring longer detection times. For example, real-time security analytics can analyze potentially dangerous IP addresses to discover previous attacks and their severity.
Batch Security
Unlike the above categories, batch security analytics applies cybersecurity to unknown attacks and attackers; after all, IT teams best handle unknown attacks batches. Batch security uses deep statistical models and large data set profiling to discover threats and remediate them. Moreover, it can help with visualizing the threats and security vulnerabilities. Machine learning is the key technology for batch security. In SureLog all alert can be easily checked by an operator which is a white box ML.
