HID Input Anomaly Detection via Behavioral Baseline
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Solution Overview
Problem
Current cybersecurity measures fail to detect and mitigate malicious code injections, particularly those using Human Interface Devices (HIDs) like USB devices, which can go undetected by antivirus programs and pose significant security risks.
Innovation Solution
A computer-implemented method that tracks user input characteristics, builds a user profile based on expected behavior, and monitors input in real-time to identify potential malicious code injections, performing mitigation processing when deviations from the baseline are detected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional antivirus programs are used to detect malicious code injections, then the system has simple detection mechanisms, but malicious code injections via HID devices go undetected
Solution Approach 1:
The detection system is segmented into multiple independent analysis components: behavioral pattern analysis, statistical anomaly detection, sequence validation, and contextual analysis. Each component analyzes specific aspects of input data independently, then results are aggregated to make a final detection decision, improving reliability without requiring a single complex detection mechanism
Solution Approach 2:
An intermediary analysis layer is introduced between the HID device and the target application. This intermediary monitors and analyzes input data streams, comparing them against learned behavioral patterns and statistical baselines before allowing data to reach the application, enabling detection of malicious injections without requiring changes to the application itself
2Reliability
If user input characteristics are tracked and analyzed in real-time to identify malicious injections, then detection accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by continuously learning and establishing baseline behavioral patterns during normal operation phases. Statistical parameters and expected input characteristics are pre-computed and stored as reference data, enabling rapid comparison and anomaly detection during actual operation without requiring intensive real-time computation
Solution Approach 2:
The system applies partial analysis to most inputs by comparing only critical characteristics against baselines, reserving more intensive full analysis for inputs that show moderate deviations. This selective analysis approach maintains low processing overhead for normal inputs while still detecting malicious injections that exhibit significant anomalies
3Reliability
If comprehensive user profile baselines are established to distinguish legitimate from malicious input, then false positive rate decreases, but system setup time and data collection requirements increase
Solution Approach 1:
A preliminary baseline establishment phase is implemented during system initialization or first-use scenarios, where the system collects and analyzes user input patterns to create initial behavioral profiles. These pre-established baselines are stored and reused for subsequent detection operations, avoiding the need to re-collect data during normal operation
Solution Approach 2:
The baseline establishment process is designed to serve multiple functions simultaneously: it creates detection thresholds, identifies user-specific patterns, and establishes statistical parameters for anomaly detection. This multi-functional approach consolidates what would otherwise require separate setup processes into a single unified phase
Data Source
AI summary
Injection attack identification and mitigation includes tracking characteristics of user input by a user to a computer system via input device(s), building and maintaining a user profile based on the tracking and that provides a baseline of expected characteristics of user input, the baseline defined by the tracked characteristics, monitoring input to the computer system in real time as the input is provided to the computer system, identifying, based on the monitoring and on a comparison of characteristics of the monitored input to the baseline of expected characteristics, a potential malicious code injection as part of the monitored input to the computer system, and performing mitigation processing based on identifying the potential malicious code injection.