Cybersecurity System Reducing False Positive Email Reports
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Solution Overview
Problem
Cybersecurity systems face inefficiencies in distinguishing between malicious and non-malicious electronic messages, leading to unnecessary processing and resource allocation for non-malicious reports, which can be costly and time-consuming for businesses.
Innovation Solution
A cybersecurity network system that includes a server generating simulated phishing messages to help users differentiate between legitimate and potentially malicious messages by analyzing structural elements, such as hyperlinks, and providing user notifications and training on message classification, thereby reducing false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If end users report potentially malicious messages, then the company's ability to detect and assess email threats improves, but non-malicious messages are also reported causing time and money loss
Solution Approach 1:
The system performs preliminary analysis of reported messages using multiple indicators (sender reputation, message content analysis, attachment scanning, hyperlink verification) before final classification. This preliminary action filters out obvious non-malicious messages early in the process, reducing the time needed for full processing of false positive reports.
Solution Approach 2:
The system introduces an intermediary classification layer between user reporting and full security processing. This intermediary automatically assesses reported messages using heuristics and known threat intelligence, acting as a mediator that can quickly dismiss non-malicious messages without requiring complete security analysis workflows.
2Reliability
If end users report potentially malicious messages, then the company's ability to detect and assess email threats improves, but processing overhead for non-malicious electronic messages increases
Solution Approach 1:
The message processing system is segmented into multiple independent analysis modules (sender verification, content analysis, attachment scanning, hyperlink checking). Each module operates independently and can be selectively applied based on initial assessment, reducing overall processing overhead by avoiding unnecessary analysis on clearly benign messages.
Solution Approach 2:
The system dynamically adjusts processing parameters based on message characteristics. For example, messages from verified internal senders skip external reputation checks, messages without attachments skip attachment scanning, and messages with clear legitimate subjects reduce content analysis depth. This parameter adaptation reduces processing overhead while maintaining detection reliability.
3Measurement precision
If the system analyzes structural elements to classify messages, then classification accuracy improves, but processing time increases
Solution Approach 1:
The system applies partial analysis based on confidence thresholds. When initial indicators strongly suggest a message is non-malicious (e.g., from a verified internal sender with no suspicious characteristics), the system performs only partial analysis rather than complete structural element examination. This reduces analysis time while maintaining sufficient classification accuracy for clear-cut cases.
Data Source
AI summary
In a cybersecurity network, a system identifies and classifies non-malicious messages by receiving a user notification indicating that the user has reported a received message as potentially malicious message, and determining whether the received message is legitimate or potentially malicious. When the system determines that the message is a legitimate, it further analyzes the message to assign a class that may include trusted internal sender, trusted external sender, or training a simulated phishing message. It will then cause the user's device to provide the user with information corresponding to the assigned class. The system may also quarantine a received message and release the message from the quarantine only after determining that the message is legitimate and receiving a user acknowledgment.