A security technician is incorporating the following requirements in an RFP for a new SIEM: -> New security notifications must be dynamically implemented by the SIEM engine -> The SIEM must be able to identify traffic baseline anomalies -> Anonymous attack data from all customers must augment attack detection and risk scoring Based on the above requirements, which of the following should the SIEM support? (Choose two.)
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A. B. C. D. E. F.BD.
The two features that the SIEM must support based on the requirements are:
B. Machine learning: The requirement for dynamically implementing new security notifications suggests that the SIEM should be capable of adapting and learning from the new patterns or trends that emerge over time. Machine learning algorithms are an effective way to enable this capability. With machine learning, the SIEM can learn from new data and update its detection algorithms and rules accordingly. Additionally, machine learning algorithms can be used to identify traffic baseline anomalies by analyzing patterns in data that deviate from the norm.
C. Multisensor deployment: The requirement for anonymous attack data from all customers to augment attack detection suggests that the SIEM should be able to collect data from multiple sources. A multisensor deployment allows the SIEM to collect data from a variety of sources, including network devices, servers, and endpoints. This approach provides the SIEM with a more complete view of the environment, which can be used to detect and respond to threats more effectively.
A. Autoscaling search capability, D. Big Data analytics, E. Cloud-based management, and F. Centralized log aggregation are not explicitly required by the RFP, although they may be beneficial features for a SIEM.
Autoscaling search capability is useful when the amount of data being processed by the SIEM changes frequently. If the volume of data being processed suddenly increases or decreases, the SIEM can automatically scale up or down to ensure that performance remains optimal.
Big Data analytics is used to analyze large volumes of data to identify patterns and trends that might not be apparent through manual analysis. This feature is useful when dealing with large amounts of data, such as log data from multiple sources.
Cloud-based management allows the SIEM to be managed from a central location, making it easier to administer and maintain. Additionally, cloud-based management provides access to additional resources, such as storage and processing power, which can be used to enhance the capabilities of the SIEM.
Centralized log aggregation is the process of collecting log data from multiple sources and consolidating it in a central location. This approach makes it easier to search and analyze log data, which can be useful for identifying security incidents and investigating security breaches.