The important question is: How can this be recognized at an early stage? It requires the continuous monitoring and analysis of internal and external network behavior. Together with the analysis of user behavior, these are two basic methods for monitoring security in corporate networks. As cyber security challenges continue to grow and become more complex, the methods used to prevent attacks and breaches of data security are becoming more sophisticated.
Network Behavior Analytics (NBA) connects all network activities and data sources. Behavior Analysis detects abnormal behavior – from unusual login times to locations where login attempts take place – and helps organizations improve security.
In the case of behavior analyses in the IT sector, the focus is increasingly on security-relevant data. The focus here is on understanding who is using a network, how this is done, and whether the activities and actions performed are permitted. The aim is to detect conspicuous behavior, abnormal behavior in the network, at an early stage.
Man vs. Machine
Technologies alone do not make a company secure. It is only the interaction of technical solutions and human expertise that can ensure optimum cyber security. The best solution is one in which technology and people work together efficiently, because fundamental questions require people, their specialist knowledge, their many years of experience, and the resulting good powers of judgment. This combination is what delivers a decisive advantage and ensures optimum security.
Behavior Analysis uses special algorithms and machine learning methods. A broad framework of data, including data from the field of log data analytics, or LDA for short, is integrated for comprehensive security access, thereby enabling attacks to be detected and blocked. With benchmarks that look like “normal” user and network behavior, actions that do not follow the usual pattern can be uncovered for further action to be taken.
The combination is the key: sophisticated analytics tools and machine learning combined with a wide variety of data sources. The goal is to improve detection rates for conspicuous behavior in the network and to keep false positives low. With this new approach, anomalies that can become risks are detected earlier. A departure from pure detection, alerting and blocking systems on the basis of threats.