NLP Emotion Model for Anomalous Employee Behavior Detection
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Solution Overview
Problem
Current security systems fail to effectively detect anomalous behavior in employees engaging in subversive activities, as they lack the ability to analyze emotional states and behavioral clues from natural language interactions, making it difficult to distinguish between normal job activities and malicious intent.
Innovation Solution
A natural language processing artificial intelligence network and data security system that determines an emotions model for users from electronic natural language interactions, using a decoder to extract textual features and an encoder to generate an emotions model, which is then used to detect anomalous behavior and implement preventive measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard network security policies are used to monitor employee interactions, then basic security monitoring is achieved, but subversive activities by errant employees cannot be detected
Solution Approach 1:
The system transforms textual interaction data into emotional state parameters by analyzing linguistic features, semantic content, and emotional indicators. This parameter transformation enables the detection of subversive activities by converting unstructured text into quantifiable emotional metrics that reveal anomalous behavior patterns inconsistent with normal job duties
Solution Approach 2:
The patent replaces manual analysis of employee interactions with an automated natural language processing system. The NLP system automatically extracts emotional states and behavioral indicators from text, eliminating the need for manual review while improving detection accuracy and scalability
2Measurement precision
If manual analysis of employee interactions is performed, then detailed examination is possible, but subversive activities remain undetected and the process is highly time-consuming
Solution Approach 1:
The system replaces manual behavioral analysis with automated NLP processing that extracts emotional states and behavioral indicators from text interactions. This substitution maintains high measurement precision by using sophisticated linguistic analysis while eliminating the time loss associated with manual review of employee communications
Solution Approach 2:
The NLP system acts as an intermediary between raw text interactions and security analysis. It processes textual data through multiple layers including tokenization, parsing, and emotional state extraction to produce refined behavioral indicators that feed into the anomaly detection system
3Reliability
If emotional state analysis is added to security monitoring, then detection of subversive activities improves, but system complexity and computational requirements increase
Solution Approach 1:
The system segments the complex task of anomaly detection into distinct modular components: text preprocessing, feature extraction, emotional state classification, and anomaly detection. Each module performs a specific function and can be independently optimized, reducing overall system complexity while maintaining high detection reliability
Data Source
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AI summary
According to an embodiment, a natural language processing artificial intelligence network and data security system determines an emotions model for one or more users from electronic natural language interactions of the users. The system includes a natural language processing decoder to determine textual features from the electronic natural language interactions that may be indicative of emotional states of the users. They system includes an emotions model encoder that generates an emotions model based on the emotional states of the users in the electronic natural language interactions retrieved from the data storage. The system also includes an artificial intelligence network and data security subsystem. The artificial intelligence network and data security subsystem may use the emotions model as a primitive for artificial intelligence based tasks including computer system security, network security, data security, proactive monitoring and preventive actions, that are moderated using the context provided by the emotional state of a user.