AI Communication Anomaly Detection for Intrusion Prevention
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Solution Overview
Problem
Existing methods for detecting and preventing computing device intrusions rely heavily on user vigilance, which is ineffective against sophisticated threats that mimic legitimate communications, leading to compromised security.
Innovation Solution
An AI-powered system with an on-device engine using natural language processing (NLP) to analyze communication patterns, detect deviations, and implement remediation actions, including blocking or alerting users of potential threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional intrusion detection methods are used that rely on user vigilance, then device complexity is reduced, but detection precision and reliability deteriorate because users cannot accurately distinguish sophisticated malicious communications from legitimate ones
Solution Approach 1:
The patent introduces an AI-based intermediary system that acts as a mediator between users and incoming communications. This intermediary analyzes communication patterns, sender behavior, and content characteristics to detect intrusions, thereby improving detection precision without requiring users to directly evaluate sophisticated threats themselves
Solution Approach 2:
The patent replaces the mechanical/manual system of user vigilance with an automated AI-based detection system. The AI engine processes communications using machine learning models to identify malicious patterns, substituting human cognitive effort with computational analysis that provides superior detection precision
2Productivity
If manual user vigilance is used for intrusion detection, then device complexity is minimized, but productivity and time efficiency worsen due to the significant time users spend evaluating suspicious communications
Solution Approach 1:
The patent implements a self-service mechanism where the AI system automatically performs intrusion detection and classification without requiring user intervention. The system serves itself by continuously learning from communication patterns and autonomously identifying threats, thereby improving productivity while managing complexity through automation
Solution Approach 2:
The patent applies preliminary action by pre-training AI models on extensive communication data before deployment. The system performs preliminary analysis of communication patterns, sender behavior, and content characteristics in advance, enabling rapid automated detection that improves productivity without proportionally increasing operational complexity
3Reliability
If automated AI-based intrusion detection is implemented, then detection precision and productivity improve, but device complexity and energy consumption increase due to the computational requirements of running AI models
Solution Approach 1:
The patent segments the AI-based detection system into modular components that can be selectively deployed. Different AI models handle different aspects of intrusion detection (e.g., sender verification, content analysis, pattern recognition), allowing the system to achieve high reliability while managing energy consumption by activating only necessary detection modules based on communication characteristics
Solution Approach 2:
The patent applies partial action by implementing tiered AI analysis where not all communications receive full AI scrutiny. The system performs preliminary filtering on obvious threats and applies more intensive AI analysis only to suspicious or high-risk communications, thereby improving reliability for critical detections while reducing overall energy consumption
Data Source
AI summary
Systems, computer program products, and methods are described herein for detection and prevention of computing device intrusion vectors. The system utilizes an AI engine that employs natural language processing (NLP) to analyze incoming communications in real-time, identifying deviations from expected patterns based on user-specific and entity-specific behaviors. The system compares these communications against learned behaviors to detect anomalies indicative of potential intrusion attempts. Upon identifying such anomalies, the system assigns a priority level to the potential intrusion and initiates remediation actions, such as blocking suspicious communications or generating alerts. Additionally, the system continuously updates its AI model based on detected anomalies to improve future detection accuracy and provides user-specific training to enhance the user's ability to recognize potential hazards. This adaptive approach offers robust protection against evolving intrusion vectors, minimizing manual intervention and enhancing overall device security.