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Anomaly detection

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While manmade correlation rules can and do detect malicious behavior, they cannot be solely relied upon to identify all threats in any given environment. Security teams are so overwhelmed by the sheer volume and sophistication of attacks that many have reached, if not exceeded, their capacity to effectively and rapidly observe, orient, decide, and take action. As a result, stealthy, hidden, and unknown threats are missed. 

Tools that analyze behavior on your network and use machine learning to find anomalies in that behavior can notify you of potential threats. Where it could take a human days or weeks to find anomalies, machine learning algorithms can find this behavior in near real-time. You can do this by adopting a user and entity behavior analytics (UEBA) platform like Splunk User Behavior Analytics, which can seamlessly integrate with Splunk Enterprise Security. Augmenting your SIEM with UBA deepens your security capabilities by detecting and resolving use cases such as lateral movement, unknown threats, and data exfiltration.

Anomaly detection using Splunk User Behavior Analytics and Splunk Enterprise Security

Splunk User Behavior Analytics (Splunk UBA) uses machine learning and your existing data in Splunk software to find anomalies that may indicate malicious behavior, such as insider threat. Splunk UBA includes a variety of indicators for suspicious or unusual user behavior that can alert your security team to investigate further. Analysts no longer have to sort through mountains of data to find out what a particular user has been up to on the corporate network. They can go to the Splunk UBA dashboard to look up any user and see all that person's behavior across all systems and machines on the network.

Splunk UBA can also augment Splunk Enterprise Security to enhance workflow and simplify investigations by synchronizing threat management across both platforms.

What are the benefits of effective anomaly detection?

Effective anomaly detection processes help you to:

  • Rapidly and effectively detect different types of threats - known, unknown, or insider - across users, devices, and applications.
  • Find anomalous behavior quickly, investigate the root cause and impact, and then quickly respond and remediate.
  • Find known, unknown, and hidden threats using multi-dimensional behavior baselines, dynamic peer group analysis, and unsupervised machine learning.
  • Visualize threats across multiple phases of an attack to give security analysts a comprehensive understanding of the root cause, scope, severity, and timeliness of an attack.

What are anomaly detection best practices?

  • Monitor insider threat/behavior analytics: Identify unusual patterns or deviations from normal behavior within your environment, enabling early detection of potential insider threats by flagging activities that may go unnoticed through traditional monitoring.
  • Detect fraud: Enhance fraud detection by automatically identifying irregularities or suspicious activities in data. This proactive approach allows you to respond swiftly to potential fraudulent behavior, minimizing financial losses and protecting the integrity of your systems.
  • Detect insider threat: Implement real-time monitoring and analysis of user behavior. By establishing baselines and recognizing deviations, you can promptly detect and respond to insider threats, ensuring the security of sensitive information and critical systems.
  • Adopt of custom ML models: Tailor anomaly detection to your specific needs. This customization ensures a more accurate identification of anomalies in data, making the anomaly detection process more effective and aligned with your unique requirements of your organization.
  • Integrate data from fraud tools:  Integrating fraud tools' data enhances the overall fraud detection process. By consolidating data sources, you can obtain a holistic view of potential fraudulent activities, leading to more robust and comprehensive fraud prevention measures.
  • Implement machine learning (ML) for fraud: ML algorithms can continuously learn and evolve, adapting to changing patterns of fraudulent behavior. This ensures a more resilient and proactive defense against fraud in comparison to static rule-based systems.
  • Perform ML model analysis: Regularly analyzing machine learning models is crucial for maintaining their effectiveness. The capabilities in Splunk security software facilitate the monitoring of model performance over time, allowing you to fine-tune and optimize your ML models for better anomaly detection accuracy and staying ahead of emerging threats.

How can Splunk Enterprise Security help with anomaly detection?

What anomaly detection processes can I put in place?