AI Log Baseline Generation for Network Anomaly Detection
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Solution Overview
Problem
Identifying potentially malicious activity in network environments is challenging due to the complexity of credentials, multiple application and network logs, and varying modes of operation, which hinders security and efficiency.
Innovation Solution
A system and method using an artificial intelligence engine to generate a baseline mode of operation from network and application logs, allowing for the monitoring and evaluation of deviations from established norms, with the capability to transmit notifications upon detection of unauthorized activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional methods are used to monitor network environments with multiple logs and credentials, then comprehensive security coverage is achieved, but the complexity of analysis and processing increases significantly
Solution Approach 1:
The patent combines multiple log sources (network logs, application logs, authentication logs) and multiple analysis functions into a single unified AI-driven platform. The system ingests diverse log formats, normalizes them, and applies integrated anomaly detection, pattern recognition, and behavioral analysis through a centralized machine learning engine, reducing the complexity of managing separate analysis tools for each log type.
Solution Approach 2:
The patent introduces AI/ML models as intermediary components between raw log data and security analysis results. These models serve as mediators that automatically process, interpret, and correlate log entries, transforming unstructured log data into meaningful security insights without requiring manual analysis of each log type, thereby reducing analysis complexity while maintaining comprehensive coverage.
2Measurement precision
If manual analysis of network logs and application logs is performed to identify malicious activity, then detailed inspection is possible, but time consumption and resource usage increase
Solution Approach 1:
The patent implements preliminary action by pre-training AI/ML models on historical log data and established security patterns before deployment. The system performs offline training and validation phases where models learn normal behavior patterns and malicious indicators in advance, enabling rapid real-time detection without manual analysis during incident response, thus reducing analysis time while maintaining high detection accuracy.
Solution Approach 2:
The patent replaces manual mechanical analysis processes with automated AI/ML-based systems. Instead of security analysts manually examining log entries, the system uses machine learning models, natural language processing, and automated correlation engines to detect anomalies and identify malicious activity, significantly reducing analysis time while maintaining or improving detection accuracy through consistent automated evaluation.
3Measurement precision
If extensive log data is collected from multiple networks and applications to establish baseline operations, then detection accuracy improves, but data processing requirements and storage needs increase
Solution Approach 1:
The patent extracts only the essential and relevant features from extensive log data for baseline establishment and anomaly detection. Instead of processing entire log files, the system identifies and extracts key attributes such as user behavior patterns, network flow characteristics, and application usage metrics, reducing the volume of data requiring continuous processing and storage while maintaining baseline accuracy through selective feature extraction.
Solution Approach 2:
The patent applies parameter changes by transforming raw log data into normalized, standardized parameters suitable for AI/ML processing. The system converts diverse log formats into unified parameter representations, applies dimensionality reduction techniques, and adjusts data granularity dynamically based on analysis needs, reducing computing resource requirements while preserving the information necessary for accurate baseline establishment and deviation detection.
Data Source
AI summary
Systems, computer program products, and methods are described herein for systems and methods of generating a baseline mode of operation from network and application logs. The present disclosure is configured to: identify a set of network logs from a set of networks and a set of application logs from a set of applications; scan the set of network logs and the set of application logs via an artificial intelligence engine; generate a baseline mode of operation from the set of network logs and the set of application logs via the artificial intelligence engine, where the baseline mode of operation includes a set of actions performed within the set of networks and the set of applications; monitor a received mode of operation from the set of networks and the set of applications; and evaluate the received mode of operation with respect to the baseline mode of operation.