Network Scanning System for Software Copyright Detection
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Solution Overview
Problem
Current technologies face challenges in detecting illegal copying of software, identifying patent infringement, and detecting malware on websites, due to the difficulty in identifying and distinguishing between different types of information on a network.
Innovation Solution
A system that uses a training corpus to train a similarity algorithm, which is then used to crawl a network and identify data usage by determining likely matches between the training corpus and identified data on the network, allowing for appropriate actions to be taken.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional detection methods are used to identify illegal copying and malware, then the detection process is simple to implement, but the detection precision and reliability are low
Solution Approach 1:
The system performs preliminary actions by training the similarity algorithm with a corpus of legitimate software and malware patterns before actual detection. This pre-training phase enables the algorithm to recognize subtle patterns and variations, significantly improving detection precision while maintaining automated operation during the actual scanning process.
Solution Approach 2:
The patent introduces a similarity algorithm as an intermediary between the raw code/data and the detection decision. This algorithm acts as a mediator that compares suspicious code against the trained corpus, providing a quantitative similarity score that improves detection accuracy without requiring complex manual analysis procedures.
2Productivity
If manual analysis is used to identify patent infringement and illegal copying, then false positives are reduced, but the productivity and speed of detection are low
Solution Approach 1:
The system implements self-service detection by automatically training the similarity algorithm and performing comparisons without requiring continuous manual intervention. The algorithm autonomously analyzes code similarities, generates detection results, and can be retrained with new data, enabling high-speed automated detection while maintaining accuracy through the trained model.
Solution Approach 2:
The patent transforms the detection problem into a parameter-based similarity measurement. By converting code comparison into quantitative similarity scores based on trained parameters and patterns, the system achieves both high processing speed through automated calculation and maintained accuracy through the statistical basis provided by the training corpus.
3Reliability
If comprehensive network scanning is performed to detect all types of illegal content, then detection coverage is improved, but the time and computational resources required increase
Solution Approach 1:
The system extracts and focuses on specific detectable features and patterns from the training corpus that are most indicative of illegal copying and malware. By identifying and extracting these key discriminative features, the system achieves comprehensive detection coverage for relevant threats while avoiding unnecessary analysis of irrelevant code sections, thereby reducing overall detection time.
Solution Approach 2:
The patent applies partial action by initially focusing detection on the most critical and commonly occurring patterns from the training corpus. The similarity algorithm prioritizes comparison against high-probability infringement patterns, achieving effective detection coverage for the most significant threats while reducing computational overhead compared to exhaustive analysis of all possible code variations.
Data Source
AI summary
A training corpus for training a similarity algorithm is retrieved. For example, the training corpus may be source code of a software application. The similarity algorithm is trained using the training corpus. A network is crawled to identify data. For example, the Internet may be randomly crawled to identify source code. The data is run through the similarity algorithm to determine a likely match between the training corpus and the identified data on the network. In response to determining the likely match between the training corpus and the identified data on the network, an action is taken. For example, the action may be to identify a particular website as containing illegally copied source code of the software application.