Network Security Database Sorting Tool for Threat Detection
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Solution Overview
Problem
Current network security systems are ineffective in promptly identifying and assessing potential threats, leading to delayed detection and resolution of security issues, which can compromise the entire network and user devices.
Innovation Solution
A network security database sorting tool that utilizes a memory to store weighted words, a translation engine to create word vectors from user-submitted messages, and a sorting engine to calculate values based on these vectors, identifying messages as threats by comparing them to a threshold, thereby sorting and filtering messages for security relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current network security systems are used to identify threats, then threat detection is performed, but detection speed and accuracy are insufficient leading to delayed resolution
Solution Approach 1:
The system segments the threat detection process into distinct functional modules: a translation engine that converts messages into word vectors, a sorting engine that calculates values and compares thresholds, and a grouping mechanism that categorizes threats. This segmentation allows each component to specialize in one aspect of analysis, improving both speed and accuracy of overall threat detection.
Solution Approach 2:
The system transforms messages from their original text form into word vectors with numerical parameters, where each word is assigned a weight and the message receives a calculated value. This parameter transformation enables quantitative comparison against thresholds, significantly improving detection accuracy and enabling faster automated decision-making about threat levels.
2Productivity
If manual analysis of security messages is performed, then detailed assessment is possible, but processing speed is too slow for effective network protection
Solution Approach 1:
The system replaces manual mechanical analysis with automated computational processing. The translation engine automatically converts messages to word vectors, and the sorting engine automatically calculates values and compares them to thresholds. This substitution of automated electronic processing for manual analysis dramatically increases productivity while maintaining consistent assessment accuracy through standardized algorithms.
Solution Approach 2:
The system creates word vectors as numerical copies of message content, where each word is represented by its weight and occurrence count. This copying transformation allows rapid computational processing of message semantics without requiring manual reading or interpretation, thereby increasing processing speed while preserving the essential meaning for accurate threat assessment.
3Reliability
If all messages are analyzed in detail, then comprehensive security assessment is achieved, but system resources are overwhelmed
Solution Approach 1:
The system performs partial analysis by focusing on key parameters extracted from messages through word vector transformation. Rather than analyzing every aspect of each message in detail, the system calculates a consolidated value based on weighted word occurrences and compares it to a threshold. This partial action approach maintains reliable threat identification while significantly reducing computational complexity and resource requirements.
Solution Approach 2:
The system extracts essential features from messages by converting them into word vectors that capture only the most relevant semantic information through weighted word selection. This extraction process removes unnecessary details and focuses computation on critical parameters, thereby maintaining assessment reliability while reducing system processing complexity and resource consumption.
Data Source
AI summary
A method comprises creating a word vector from a message, wherein the word vector comprises an entry for each word of a plurality of words, and wherein each word of the plurality of words is assigned a weight. The method further comprises calculating a value for the word vector based on each entry of the word vector and the weights assigned to the plurality of words, and identifying that the message belongs to a first group by comparing the value for the word vector to a threshold. The word vector comprises an entry for each word of a plurality of words, and wherein each word of the plurality of words is assigned a weight.