Malicious Content Detection via Simulated User Interaction
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Solution Overview
Problem
Existing malware detection systems require user interaction to analyze malicious content, which is inefficient and can lead to delays in detecting potentially harmful software, and they struggle with polymorphic malware that mutates, evading signature-based detection.
Innovation Solution
A malicious content detection system that simulates user interactions within a virtual environment, allowing automated analysis of GUI events without actual user involvement, using techniques such as scripting or hooking into operating system APIs to intercept and manipulate user interactions, enabling continuous detection without interruption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user interaction is required to analyze malicious content, then detection accuracy can be improved, but detection efficiency and speed deteriorate
Solution Approach 1:
The system performs self-service by automatically simulating user interactions through bot automation. The bot autonomously navigates GUI events, clicks buttons, and interacts with malicious content without human intervention, enabling the detection system to serve itself and eliminate the bottleneck of manual user interaction while maintaining detection accuracy
Solution Approach 2:
The system performs preliminary action by pre-configuring bot automation scripts that can simulate various user interaction patterns before actual detection begins. These pre-programmed interaction sequences are ready to execute immediately when malicious content is detected, eliminating waiting time and enabling continuous automated detection workflows
2Adaptability or versatility
If manual user interaction is used for malware analysis, then complex GUI events can be handled, but continuous detection is interrupted
Solution Approach 1:
The bot automation system enables self-service by automatically handling all GUI event interactions without requiring human operators. The bot can independently navigate complex interfaces, respond to prompts, and continue analysis through multiple GUI events, allowing detection to proceed continuously without interruption by manual interaction requirements
Solution Approach 2:
The system achieves continuity of useful action by designing bot automation that can operate indefinitely without pause. The bot maintains continuous execution of detection tasks, automatically transitioning between different analysis stages and handling GUI events as they occur, ensuring uninterrupted detection workflows and eliminating idle time between manual interactions
3Speed
If signature-based detection is used, then known malware can be identified quickly, but polymorphic malware evades detection
Solution Approach 1:
The system applies dynamics by transitioning from static signature matching to dynamic behavior analysis. The bot automation observes and analyzes the runtime behavior of suspicious files, including how they interact with the GUI, system resources, and user interfaces. This dynamic approach adapts to polymorphic malware by focusing on behavioral patterns rather than fixed code signatures, maintaining both speed and reliability
Solution Approach 2:
The system replaces the mechanical signature-matching process with a more sophisticated behavioral analysis mechanism. Instead of relying on rigid pattern matching that fails against mutated code, the bot automation employs intelligent observation and analysis of runtime characteristics, substituting the outdated mechanical detection approach with an adaptive behavioral assessment system
Data Source
AI summary
Techniques for detecting malicious content using simulated user interactions are described herein. In one embodiment, a monitoring module monitors activities of a malicious content suspect executed within a sandboxed operating environment. In response to detection of a predetermined event triggered by the malicious content suspect requesting a user action on a graphical user interface (GUI) presented by the malicious content suspect, simulating, a user interaction module simulates a user interaction with the GUI without user intervention. An analysis module analyzes activities of the malicious content suspect in response to the simulated user interaction to determine whether the malicious content suspect should be declared as malicious.