Federated Search AI for Real-Time Threat Detection
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Solution Overview
Problem
Current cybersecurity systems face delays in updating threat models during searches, making it challenging for security analysts to identify and focus on suspicious behavior in real-time, especially in federated search environments.
Innovation Solution
A computer-implemented method that receives cyber threat information, captures it in a STIX bundle, and applies an analytic pipeline with classification and clustering models to identify suspicious features, which are then annotated and used to train a machine learning model incrementally during data exploration, assisting security analysts in identifying threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If a search process is ongoing, then security analysts can investigate security incidents, but there is a delay in updating the model of current security threats
Solution Approach 1:
The system implements feedback by continuously capturing suspicious features identified during search processes and using them to retrain the machine learning model. This creates a closed-loop system where the model is updated with real-time information about emerging threats, allowing it to adapt its detection capabilities during ongoing security investigations without requiring manual intervention or waiting for periodic updates.
Solution Approach 2:
The machine learning model transitions from a static, pre-trained state to a dynamic, continuously learning system. The model is retrained incrementally using suspicious features captured during search processes, enabling it to adapt to evolving threat landscapes in real-time. This dynamic update mechanism allows the system to maintain high detection accuracy while reducing the delay between threat emergence and model adaptation.
2Productivity
If security analysts search for known threats, then they can identify anomalous behavior, but the search process is time-consuming and lacks real-time focus
Solution Approach 1:
The system performs self-service by automatically capturing, classifying, and clustering suspicious features without requiring manual analyst intervention. The machine learning model autonomously processes search results, identifies patterns, and generates focus areas for further investigation. This automation significantly increases the speed of threat detection while reducing the operational complexity for security analysts.
Solution Approach 2:
The search process is segmented into distinct automated stages: capturing suspicious features, classifying them using machine learning models, clustering related features, and generating focus areas. This segmentation allows each component to be optimized independently and enables the system to process large volumes of data efficiently, improving productivity without overwhelming complexity.
3Reliability
If the model is updated in real-time, then threat detection accuracy improves, but the system complexity and computational resources increase
Solution Approach 1:
Instead of complete model retraining from scratch, the system applies partial action by updating only the relevant portions of the machine learning model using incremental learning techniques. The model selectively incorporates new suspicious features and patterns without processing the entire training dataset, significantly reducing computational requirements while maintaining high detection accuracy.
Solution Approach 2:
The system manages model complexity by changing parameters such as the learning rate, batch size, and feature selection criteria during incremental updates. These parameter adjustments allow the model to adapt to new threat patterns efficiently without requiring proportional increases in computational resources or system complexity.
Data Source
AI summary
A computer-implemented method includes receiving CTI from a data source during a search of a system, and capturing the CTI in a STIX bundle. The method includes invoking an analytic pipeline on the STIX bundle that includes applying a classification model on the STIX bundle to classify features from the CTI and applying a clustering model on the STIX bundle to identify a cluster of features from the CTI. The output of the analytic pipeline is analyzed to identify suspicious features that include a combination of the classified features and the cluster of features. The suspicious features are annotated thereby highlighting risk and threat, and attack techniques are identified using existing domain expertise encoded as heuristics to provide additional machine learning features.