ML Email Classification for Automated Benign Alert Disposal
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Solution Overview
Problem
The challenge in cybersecurity is efficiently scaling threat detection and response capabilities to handle the increasing volume of security threats in cloud-based computing environments without causing technical inefficiencies that hinder the detection and response to non-malicious electronic communications.
Innovation Solution
A computer-implemented method using a machine learning-based electronic communication classification model to identify and automatically route non-malicious electronic communications to a disposal queue, bypassing unnecessary cybersecurity investigations and reducing the workload on security analysts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If security operation services scale to handle increasing volume of security threats, then threat detection capability is improved, but technical inefficiencies increase that slow down detection and response
Solution Approach 1:
The patent segments security alerts into different categories (phishing alerts, malware alerts, etc.) and processes them through different automated workflows. This segmentation allows the system to handle high volumes of alerts efficiently by routing them to specialized processing paths, improving overall detection capability while maintaining response time through targeted automation rather than uniform processing of all alerts.
2Measurement precision
If all electronic communications are investigated for security threats, then detection accuracy is improved, but workload on security analysts increases
Solution Approach 1:
The patent implements preliminary automated analysis of electronic communications using machine learning models and feature extraction before human analyst review. This preliminary action filters out obviously benign communications and prepares structured data for analysts, maintaining detection accuracy by pre-identifying suspicious patterns while reducing analyst workload by eliminating the need for manual review of clearly malicious or obviously benign communications.
Solution Approach 2:
The patent introduces an intermediary automated classification system that sits between incoming electronic communications and human analysts. This intermediary uses feature extractors and classification models to preliminarily assess communications, providing analysts with pre-processed information and confidence scores. This intermediary layer maintains detection accuracy by catching sophisticated threats while reducing complexity by handling routine classifications automatically.
3Measurement precision
If machine learning models analyze all electronic communications, then classification accuracy is improved, but processing speed decreases
Solution Approach 1:
The patent applies partial machine learning analysis by using feature extractors to analyze only specific aspects of electronic communications (sender reputation, message content patterns, attachment analysis) rather than performing exhaustive full-content analysis on every message. This partial action maintains classification accuracy for suspicious communications while improving processing speed by avoiding redundant analysis of obviously benign features in high-volume traffic.
Solution Approach 2:
The patent implements local quality by applying different levels of analysis depth to different communications based on initial screening results. High-priority or suspicious communications receive full machine learning analysis with multiple feature extractors, while low-priority communications receive lighter processing. This local differentiation maintains accuracy for critical cases while improving overall processing speed through selective deep analysis.
Data Source
AI summary
A system and method for accelerating a disposition of non-malicious electronic communications includes extracting one or more corpora of feature vectors from an electronic communication based on providing the electronic communication as input to a feature extractor; computing, by a machine learning-based electronic communication classification model, an electronic communication-type classification inference that includes a probability of the electronic communication being of the target non-malicious electronic communication type in response to the machine learning-based electronic communication classification model receiving the one or more corpora of feature vectors; attributing a classification label of the target non-malicious electronic communication type to the electronic communication based on the probability of the electronic communication-type classification inference satisfying a minimum electronic communication classification threshold; and automatically routing a security alert associated with the electronic communication to an alert disposal queue based on the electronic communication having the classification label of the target non-malicious electronic communication type.