Fraudulent Software Detection via Behavioral Image Analysis
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Solution Overview
Problem
Current computer security systems are unable to detect malicious software programs that misrepresent legitimate computer operations, as they rely on prior knowledge of signatures and cannot identify fraudulent activities that mimic legitimate software, leading to potential system disruptions and data theft.
Innovation Solution
A method and apparatus using processors to monitor internet activity, compare it with suspicious feature information, and identify fraudulent software by recognizing images that misrepresent legitimate operations, thereby mitigating associated activities and preventing malicious software execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If security software programs use signature-based detection methods, then they can detect known malicious software programs, but they are unable to detect malicious software programs for which the signature is unknown or that misrepresent legitimate computer operations
Solution Approach 1:
The system performs preliminary analysis of software behavior patterns and characteristics before execution, creating a baseline profile that enables detection of both known and unknown malicious software. This preliminary action includes analyzing code structure, import tables, and behavioral signatures to establish detection criteria in advance.
Solution Approach 2:
The detection system transitions from static signature matching to dynamic behavior analysis, continuously monitoring software execution patterns and adapting detection criteria based on observed behavior. This dynamic approach allows the system to identify malicious software that attempts to mimic legitimate operations by analyzing actual runtime behavior rather than relying solely on pre-defined signatures.
2Measurement precision
If security software programs rely on prior knowledge of signatures, then they can identify specific malicious software, but they cannot identify legitimate computer operation misrepresentation or zero-day threats
Solution Approach 1:
The system introduces behavioral analysis as an intermediary layer between the software and the detection engine. This intermediary analyzes the software's execution patterns, system calls, and operational characteristics to generate detection indicators, enabling the system to identify malicious software that attempts to disguise itself as legitimate operations without requiring prior knowledge of specific malware signatures.
Solution Approach 2:
The system replaces the mechanical signature-matching approach with a more sophisticated behavioral analysis mechanism. Instead of simply comparing software against a database of known signatures, the system monitors and analyzes software behavior patterns, making the detection process more adaptable to novel threats while maintaining precision in identifying malicious activities.
3Reliability
If security software programs use comprehensive analysis methods, then they can detect various types of malicious software, but the complexity of the detection system increases
Solution Approach 1:
The detection system is divided into multiple specialized modules, each responsible for analyzing specific aspects of software behavior. This segmentation includes separate components for code analysis, behavior monitoring, pattern recognition, and threat classification. By dividing the complex detection task into manageable segments, the system achieves comprehensive detection capability while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The system employs a multi-functional detection framework where a single integrated platform performs multiple detection functions including signature matching, behavioral analysis, pattern recognition, and threat assessment. This universal approach consolidates various detection methods into one system, reducing overall complexity while maintaining comprehensive detection effectiveness across different types of malicious software.
Data Source
AI summary
A method for detecting legitimate computer operation misrepresentation is provided. In some embodiments, the method comprises monitoring internet activity associated with a user computer, comparing the internet activity with suspicious feature information to produce a comparison result, wherein the suspicious feature information comprises at least one image that misrepresents at least one legitimate computer operation, identifying fraudulent software based on the comparison result, wherein the comparison result indicates a portion of the internet activity that misrepresents the at least one legitimate computer operation and mitigating activities associated with the identified fraudulent software.