Communication Metadata Scoring for Malicious Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing security measures are ineffective in detecting malicious communications, particularly when malicious code is hidden within attachments, as they rely on file extensions and do not account for evolving techniques, leading to security breaches and resource consumption issues.
Innovation Solution
A computer-implemented method that intercepts communications, extracts metadata, determines if a file is attached, and scores fields in both communication and file metadata using an algorithm developed from previous communications to predict whether the communication is malicious, with the ability to update the algorithm based on feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If security measures rely on file extensions to determine whether to block communications, then the security system is simple to operate, but the detection accuracy is low and malicious communications can easily bypass security measures
Solution Approach 1:
The patent segments the analysis of communications into multiple independent metadata fields (file type, file size, communication type, subject line, body content, attachment properties). Each field is scored separately and then aggregated, allowing the system to examine multiple dimensions of a communication without creating a monolithic complex system. This segmentation enables comprehensive analysis while maintaining modularity.
Solution Approach 2:
The patent creates a universal scoring algorithm that can evaluate multiple types of metadata fields across different communication formats (email, instant message, text message). The same algorithmic framework handles diverse field types (file properties, communication metadata, content analysis) uniformly, making the system adaptable to various communication channels and file types without requiring separate specialized systems.
2Reliability
If security measures use simple file extension checking, then the system requires minimal computational resources, but it fails to detect malicious code hidden within attachments
Solution Approach 1:
The patent applies partial action by scoring only the most relevant metadata fields for each communication rather than analyzing every possible attribute in equal detail. The system dynamically determines which fields to emphasize based on the communication type and detected patterns, performing deeper analysis only where necessary. This selective approach maintains high detection reliability while avoiding unnecessary computational expenditure on less relevant fields.
Solution Approach 2:
The patent changes parameters by dynamically adjusting the weights and thresholds of metadata field scores based on learned patterns from previous communications. The algorithm adapts its sensitivity to different field types depending on the context, such as emphasizing file type matching for certain communications while prioritizing subject line analysis for others. This dynamic parameter adjustment optimizes resource usage while maintaining detection effectiveness.
3Adaptability or versatility
If security measures use static classification rules, then the system is easy to implement, but it cannot adapt to evolving malicious techniques
Solution Approach 1:
The patent implements feedback by using the actual classification outcomes and security analyst decisions on previous communications to refine and update the scoring algorithm. The system learns from confirmed malicious and benign communications, adjusting field weights and thresholds to improve future detection accuracy. This continuous feedback loop enables the system to adapt to new malicious techniques while maintaining a manageable algorithmic structure through systematic learning rather than ad-hoc complexity.
Solution Approach 2:
The patent introduces dynamics by making the classification algorithm adaptive and evolving rather than static. The scoring weights, field relevances, and decision thresholds are not fixed but can be adjusted based on accumulated data and security trends. This dynamic approach allows the system to respond to evolving malicious techniques by continuously refining its classification criteria while maintaining algorithmic coherence through structured learning mechanisms.
4Measurement precision
If security measures analyze multiple metadata fields with dynamic scoring, then the classification accuracy improves, but the processing time and system complexity increase
Solution Approach 1:
The patent applies partial action by focusing computational effort on the most discriminative metadata fields for each communication type. Rather than equally analyzing all possible fields, the system identifies and prioritizes the subset of fields that provide the most classification value for that specific communication, scoring only those in detail while giving lesser weight to others. This selective deep analysis maintains high accuracy while reducing overall processing time.
Solution Approach 2:
The patent applies local quality by tailoring the depth and type of analysis to each specific metadata field and communication context. Different fields receive different levels of scrutiny based on their relevance to detecting malicious communications of that type. For example, file type fields may receive intensive analysis for email attachments while communication timing fields receive more attention for instant messages. This localized quality adjustment optimizes processing efficiency while maintaining classification accuracy.
Data Source
AI summary
In accordance with particular embodiments, a method includes intercepting a communication and extracting metadata associated with the communication. The extracted metadata comprises a plurality of different fields from communication metadata and file metadata. The method further includes determining a score, based on previous communications, for each field of the extracted metadata. The score is indicative of a likelihood that the communication is a malicious communication. The method additionally includes combining the scores to generate a combined score for the communication based on an algorithm developed from the previous communications. The method also includes generating, based on the combined score at a first time, a predicted classification as to whether the communication is a malicious communication. The method further includes receiving, at a second time subsequent to the first time, an indication of whether the communication is a malicious communication and updating the algorithm based on the indication.