Signature Quality Analysis for False-Positive Reduction

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Solution Overview

Problem

Existing signature-based threat detection systems suffer from subjective evaluation methods, leading to false positives due to the inherent subjectivity in assessing signature quality, which disrupts network activity and inconveniences administrators.

Innovation Solution

Implement systems and methods to objectively evaluate signature quality by analyzing connection data, including IP addresses and domain reputations, to determine if a signature is prone to false positives, and apply policies to prevent misclassification of network activity as malicious.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If subjective evaluation methods are used to assess signature quality, then the evaluation process is simple, but false positives increase and network activity is disrupted

Engineering Contradiction:

Improveevaluation process simplicityVSAvoidsignature quality assessment accuracy

Core Design Contradiction:

Ease of operationVSReliability

Solution Approach 1:

The patent replaces subjective human evaluation (mechanical/manual system) with an automated objective evaluation system that uses machine learning models and algorithms to assess signature quality, thereby eliminating false positives while maintaining operational simplicity

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The evaluation system automatically assesses signature quality without human intervention by using connection data and machine learning models to generate quality scores and determine false positive risk, enabling the system to self-evaluate and self-improve

Inventive Principle:

Principle #25Self-service

2Productivity

If signature quality is not evaluated objectively, then the system operates with fewer constraints, but false positives disrupt network activity and inconvenience administrators

Engineering Contradiction:

Improvenetwork activity flowVSAvoidfalse positives

Core Design Contradiction:

ProductivityVSObject-affected harmful factors

Solution Approach 1:

The system performs preliminary objective evaluation of signature quality before deployment using connection data and machine learning models to predict false positive risk, preventing harmful false positives from occurring in the first place

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system uses feedback from connection data analysis and machine learning model predictions to continuously improve signature quality assessment, adjusting evaluation criteria based on observed false positive patterns and network behavior

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12432257B2Signature quality evaluation

Publication Date: 2025.09.30 SOPHOS LTD

AI summary

Systems and methods for scanning network activity. The methods include receiving at an interface connection data regarding a plurality of network connections, wherein the connection data includes a signature used to classify each of the plurality of network connections; determining, using one or more processors executing instructions stored on memory to provide a signature analysis engine configured to analyze the connection data, the signature is prohibitively prone to misclassifying network activity as malicious, wherein the determination is based on the analysis of the connection data; and implementing a signature policy to prevent the signature from misclassifying network activity as malicious.