Neural Network API Request Analysis for Insider Threat Detection
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Solution Overview
Problem
Existing insider threat detection methods in enterprise computing systems, particularly in large-scale software systems, face challenges in detecting malicious activities by authorized users, as they often rely on rules-based systems that are difficult to tune and may not perform well during normal business hours or in remote/hybrid work environments, where schedule-based rules are ineffective and IP address management becomes complex.
Innovation Solution
A neural network-based API request pattern analysis system that analyzes real-time API requests using a machine learning model to determine suspicious activity by comparing the current request with recent requests, flagging anomalies and automatically disabling user access if a threshold of suspicious requests is reached, thereby enhancing real-time threat detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rules-based systems are used for insider threat detection, then detection coverage is provided, but the system requires continuous tuning and has high complexity in managing IP address whitelists
Solution Approach 1:
The patent replaces the mechanical rules-based system with a machine learning model that automatically learns detection patterns from data. The neural network analyzes API request sequences and user behavior patterns without requiring manual rule configuration, thereby substituting the complex mechanical tuning process with an automated learning system that adapts to changing threat patterns.
Solution Approach 2:
The machine learning model performs self-service by automatically training on historical data and adapting to new threat patterns without human intervention. The system continuously learns from incoming API requests and updates its detection capabilities autonomously, eliminating the need for security personnel to manually tune rules and manage IP address whitelists.
2Reliability
If schedule-based rules are used for threat detection, then normal business hours protection is provided, but the system fails to detect threats during remote work and hybrid environments
Solution Approach 1:
The patent implements dynamic detection by analyzing actual user behavior patterns rather than relying on static schedule-based rules. The machine learning model adapts to each user's typical API request patterns, timing, and sequences, allowing it to detect anomalies whether the user works during traditional business hours or remotely. This dynamic approach automatically adjusts to hybrid work environments without requiring rule modifications.
Solution Approach 2:
The machine learning model provides universal detection capability that works across all work environments (office, remote, hybrid) and all users simultaneously. Instead of requiring separate rules for different work scenarios, the single model learns from diverse user patterns and applies detection universally, making the system versatile across various work arrangements.
3Ease of operation
If IP address whitelists are used for security management, then access control is provided, but management becomes difficult in remote and hybrid work environments
Solution Approach 1:
The patent replaces the mechanical IP address whitelist management system with a machine learning-based user behavior analysis system. Instead of tracking and managing IP addresses, the model focuses on analyzing API request patterns, sequences, and user authentication behaviors. This substitution eliminates the complexity of IP address management in remote work environments while maintaining effective access control through behavioral anomaly detection.
4Speed
If real-time analysis of API requests is performed, then threat detection speed is improved, but computational resources and system complexity increase
Solution Approach 1:
The patent applies partial analysis by focusing on specific API request sequences and user behavior patterns rather than analyzing all system data in real-time. The machine learning model processes only the relevant features of API requests (user identification, request sequences, timing patterns) needed for threat detection, performing sufficient analysis to detect threats without the excessive computational burden of comprehensive real-time monitoring of all system operations.
Data Source
AI summary
A method of neural network-based pattern analysis for real-time threat detection according to an embodiment includes receiving a real-time request for a system resource from a user of the system, determining a user identifier associated with the user of the system, retrieving a set of recent requests associated with the user identifier from a short-term buffer, analyzing, using machine learning, the real-time request based on the set of recent requests and a neural network model to determine whether the real-time request is suspicious, and flagging the real-time request as a suspicious request in response to a determination that the real-time request is suspicious.