Semantic Email Classification for Phishing and Malware Detection
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Solution Overview
Problem
Existing systems are ineffective in accurately identifying and mitigating phishing and malware attacks in electronic messages, particularly through email, due to the deceptive nature of these attacks and the complexity of analyzing various components within the messages.
Innovation Solution
A multi-phase analysis system utilizing machine learning models, neural networks, and natural language processing to analyze email bodies, subjects, attachments, and URLs, followed by a probabilistic generative model and neural network to determine malicious intent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional email filtering systems are used, then the system is simple and fast, but the accuracy of identifying phishing and malware attacks is low
Solution Approach 1:
The system segments the email analysis into multiple independent phases: Phase 1 analyzes email body and subject using machine learning, Phase 2 analyzes attachments and URLs using NLP, and Phase 3 performs semantic analysis on header information. Each phase produces separate results that are later combined, allowing complex analysis to be broken down into manageable components that can be processed independently and efficiently
Solution Approach 2:
The system implements a nested architecture where a neural network classifier is embedded within the multi-phase analysis framework. The results from all three phases are provided as input to this nested neural network, which synthesizes the information to determine whether the email is malicious. This nested structure allows the complex classification task to be performed by a dedicated component that receives pre-processed information from multiple specialized analysis phases
2Reliability
If comprehensive multi-phase analysis is performed, then the accuracy of detecting malicious emails improves, but the processing time and computational resources increase
Solution Approach 1:
By dividing the analysis into three parallel phases that can be executed concurrently or in optimized sequences, the system reduces overall processing time compared to sequential single-phase analysis. Each phase focuses on specific email components, allowing for specialized optimization of each analysis stream
Solution Approach 2:
The system performs preliminary analysis on different email components in parallel during the three phases, extracting features and generating results before the final classification stage. This preliminary processing of email body, attachments, URLs, and headers occurs concurrently, preparing information in advance for the neural network classifier to synthesize into a final determination
Data Source
AI summary
A computerized method is disclosed that includes operations of obtaining and parsing a copy of an email into components including body and subject line information, performing a plurality of analyses on the copy of the email including deploying a probabilistic generative model resulting in a determination of a likelihood that the email is directed to one of a predefined set of topics, classifying semantics extracted from the body or subject based on results generated by the probabilistic generative model, and performing a determination operation by a relationship compiler or a neural network resulting in a maliciousness determination as to whether the email is malicious. Responsive to the maliciousness determination being malicious, generating a graphical user interface display indicating that the email has been classified as malicious and performing a mail retrieval attempt that causes the email to be removed from an inbox of the mail client of the intended recipient.