Transformer Sequence Prediction for Novel Cyber Threat Detection
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Solution Overview
Problem
Legacy cyber threat defense tools are inadequate in detecting novel and evolving threats, failing to account for subtle changes in attacks and insider threats, and are resource-intensive, often requiring human intervention that can compromise network devices.
Innovation Solution
A cyber threat defense system utilizing AI models, including a modeler and predictor configured to analyze network data as a sequence of events, employing Transformer deep learning models to predict anomalies and generate notifications with context, and a user agent predictor classifier to identify user agents with confidence scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional legacy security tools using predefined rules and signatures are used, then implementation and deployment are straightforward, but detection capability against novel and evolving threats is insufficient
Solution Approach 1:
The patent replaces traditional mechanical rule-based security systems with AI/ML-based predictive analytics. The system uses machine learning models to analyze network traffic patterns and predict potential threats, substituting the rigid mechanical approach of predefined signatures with adaptive intelligent systems that can detect novel attacks without requiring pre-programmed rules.
Solution Approach 2:
The system dynamically changes detection parameters by using AI models that adapt to evolving threat patterns. Instead of fixed signature parameters, the system employs machine learning algorithms that continuously learn from new data, adjusting detection thresholds and patterns to maintain effectiveness against emerging threats while reducing false positives.
2Reliability
If AI-based predictive analytics are implemented, then detection of novel threats and insider threats is improved, but system complexity and computational requirements increase
Solution Approach 1:
The patent segments the security system into distinct functional modules: data collection components, AI/ML model training modules, predictive analytics engines, and response automation systems. This segmentation allows each component to be optimized independently and deployed in a distributed architecture, reducing overall system complexity while maintaining advanced detection capabilities.
Solution Approach 2:
The system introduces AI/ML models as intermediary layers between raw network data and security decisions. These intermediary models process and interpret complex network patterns, translating raw data into actionable threat assessments, thereby simplifying the overall system architecture by centralizing complex analytical functions in dedicated intermediary components.
3Measurement precision
If manual vulnerability scanning is performed, then security assessment is conducted, but network resources are consumed and false positives occur
Solution Approach 1:
The system performs preliminary AI-based analysis of network traffic patterns before conducting traditional vulnerability scans. By pre-identifying suspicious activities and potential vulnerabilities through predictive analytics, the system can prioritize and target scans more precisely, reducing unnecessary scanning of healthy systems and minimizing resource consumption while maintaining high detection accuracy.
Data Source
AI summary
A cyber threat defense system and a method for detecting a cyber threat may use a predictor, e.g. a Transformer deep learning model, which is configured to predict a next item in the sequence of events and to detect one or more anomalies in the sequence of events. This provides a notification comprising (i) information about the one or more anomalies; and (ii) a prediction of what would have been expected.