Cybersecurity experts have increasingly come to expect more than just a powerful feature set from products in the market. They now seek innovative solutions that not only offer comparable capabilities but also enable them to extract new insights. This shift in expectation has elevated the significance of data modelling, as well as the application of machine learning and statistical methodologies, within the realm of security.
But what if you don’t have machine learning or data modelling experience? How do we get started? Or what if you ̇want to apply specific modelling to the data?
The Signal analytics module is a powerful feature within the Resolution Intelligence platform that empowers security professionals to analyze and gain insights from various signals generated by behavior-based detections, YARA-L based detections, exposure-based detections, and threat intelligence feeds. By consolidating these signals and providing advanced analytical capabilities, Signal analytics enables users to uncover hidden patterns, correlations, and anomalies, thereby enhancing threat detection and response.
Users interact with the Signal analytics module through a dedicated graphical interface. The interface presents signals as data points in a graph, with a filter on the right side that shows trends. Users can apply filters to select specific data on the left side, refining their analysis. The trend analysis functionality helps users understand the trends associated with the selected data. Once users have selected data, they can apply different machine learning models, such as clustering, time series analysis, or analysis based on the MITRE framework. These models are presented as a dropdown menu, enabling users to apply them to the selected data with a single click.
By leveraging Signal analytics in the Resolution Intelligence platform, security professionals can gain valuable insights from signals generated by different detection systems. The ability to filter, select, and apply machine learning models enhances their analysis capabilities, ultimately improving threat detection, log source optimization, and overall security operations. Let's get started with Signal Analytics.
Note: A signal represents the outcome or result of multiple detection systems and provides valuable information about potential security events or anomalies.
Purpose of Signal Analytics
The primary purpose of the Signal analytics module is to provide security teams with a centralized platform for analyzing and interpreting signals. This module plays a crucial role in transforming raw signals into actionable insights, allowing organizations to identify and mitigate security risks proactively. By leveraging advanced analytics – the ability to slice and dice data, perform trend analysis, and apply machine learning models on Signals, Signal analytics facilitates security professionals with informed decision-making, continuous improvement, and staying ahead of emerging threats(by identifying new attack patterns).
Here are some ways how Signal analytics can bring value to security operations:
- Actionable Insights and Incident Response: Integrating machine learning models within Signal analytics uncovers valuable insights within the collected signals. During an incident response investigation, signal analytics helps analysts sift through a large volume of signals to identify relevant ones for deeper analysis. By applying machine learning models, analysts can quickly identify indicators of compromise or patterns indicative of an ongoing attack.
- Maximizing Security Operations Efficiency: Signal analytics helps optimize resource allocation by identifying noisy log sources and fine-tuning configurations. By reducing false positives and allocating resources more efficiently, organizations can maximize their security resources' effectiveness.
- Staying Ahead of Emerging Threats: Signal analytics equips security professionals with the tools to stay ahead of evolving threats. By analyzing signals and leveraging machine learning models, teams can identify emerging attack patterns, potential vulnerabilities, and indicators of compromise, allowing them to proactively adapt their security strategies.
- Compliance Monitoring: Security teams can leverage signal analytics to monitor signals related to compliance requirements. By applying machine learning models, they can identify deviations or patterns that violate compliance standards and take appropriate actions to address them.