Automated Labeled Data Generation from Cyberattack Logs
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Solution Overview
Problem
Existing AI-based cyber security systems face challenges in obtaining high-quality labeled datasets for training due to the high cost and error-prone manual labeling processes, leading to inadequate detection and response to sophisticated cyber threats.
Innovation Solution
An automated process simulates cyberattacks against a target network to generate accurate labeled training data from attack log files, using breach and attack simulation tools to ensure up-to-date tactics, and adapts the AI model to the specific network environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling processes are used to create training datasets, then data accuracy can be maintained, but the cost and time consumption increase significantly
Solution Approach 1:
The system performs automatic labeling of security events by using the security model to identify and label attack patterns in event data without human intervention. The labeled dataset is then used to train and update the security model, creating a self-improving system that eliminates manual labeling while maintaining accuracy through automated pattern recognition.
Solution Approach 2:
The system creates synthetic attack scenarios by generating simulated attack data that mirrors real attack patterns. These synthetic labeled datasets are created by copying and adapting known attack patterns, allowing the model to be trained on diverse attack scenarios without requiring manual labeling of every possible attack instance.
2Productivity
If generic AI models are used for cyber threat detection, then deployment speed is improved, but detection accuracy for specific network environments deteriorates
Solution Approach 1:
The system performs preliminary training of the security model using historical security events and attack patterns specific to the target network environment before deployment. This pre-adaptation phase allows the model to learn environment-specific characteristics in advance, ensuring both rapid deployment and high detection accuracy from the start.
Solution Approach 2:
The system dynamically adjusts model parameters and thresholds based on the specific characteristics of the network environment being protected. By adapting parameters such as detection thresholds, event weighting, and attack pattern priorities to match the unique profile of each network, the model achieves optimal detection accuracy while maintaining fast deployment through automated parameter tuning.
3Reliability
If comprehensive security monitoring is implemented, then threat detection capability is improved, but system performance and network bandwidth are burdened
Solution Approach 1:
The system extracts only the most critical and relevant features from security events for analysis, rather than processing complete event data. By identifying and extracting key attributes such as attack patterns, source/destination addresses, and anomaly scores, the system maintains high detection capability while significantly reducing computational overhead and preserving network performance.
Solution Approach 2:
The system implements selective monitoring that focuses computational resources on analyzing events with higher suspiciousness scores or those matching known attack patterns. Rather than uniformly analyzing all events at full depth, the system applies partial analysis to low-risk events and comprehensive analysis only to high-risk events, optimizing the balance between detection capability and system performance.
Data Source
AI summary
Described are techniques for automated generation of labeled datasets for training an AI model to identify a cyberattack. The techniques include receiving configuration information for simulating a cyberattack against a target computer network. The techniques further include executing a cyberattack simulation, based on the configuration information, against the target computer network, where one or more attack log files containing information related to the cyberattack simulation are generated by resources of the target computer network in response to the cyberattack simulation. The techniques further include generating labeled training data from the one or more attack log files to correspond to specifications of the target computer network, and training an artificial intelligence (AI) model to identify the cyberattack in the target computer network using the labeled training data.