Messaging Server Alerts for Real-Time Malicious Message Screening
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Solution Overview
Problem
Existing cybersecurity solutions struggle to provide timely and relevant information to end users about potentially malicious messages, particularly in mobile computing environments with limited processing power and network bandwidth, while balancing resource consumption.
Innovation Solution
A computing platform monitors messages in real-time, identifies potentially malicious content using machine learning models, and generates instream alerts to messaging servers, which deliver warnings to intended recipients before or alongside the malicious messages, and also notifies external entities and system logs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time message monitoring is implemented to identify malicious messages, then security protection effectiveness is improved, but computing resource consumption increases
Solution Approach 1:
A protection server is introduced as an intermediary between the messaging server and user devices. The protection server performs machine learning-based analysis of messages to identify phishing and spam content, generating alert messages that are injected into the message stream. This intermediary approach shifts the computational burden from resource-constrained user devices to a centralized server with sufficient processing power, thereby maintaining security effectiveness while preserving device resource efficiency.
2Loss of time
If real-time message monitoring is implemented to identify malicious messages, then detection timeliness is improved, but network bandwidth consumption increases
Solution Approach 1:
The system extracts only the essential message content and metadata needed for malicious message detection, rather than transmitting entire message streams for analysis. The protection server receives selected message data, performs rapid machine learning-based classification to identify phishing and spam messages, and generates compact alert messages. This extraction approach reduces network bandwidth consumption while maintaining real-time detection capability.
3Measurement precision
If comprehensive message analysis is performed to improve detection accuracy, then measurement precision is improved, but device processing power requirements increase
Solution Approach 1:
The protection server serves as an intermediary that performs comprehensive machine learning-based message analysis using sophisticated models for detecting phishing and spam. Instead of requiring user devices to execute these computationally intensive analysis algorithms, the protection server handles all heavy processing tasks and returns simplified results. This architecture enables high detection accuracy through comprehensive analysis while keeping user device processing power requirements minimal.
Data Source
AI summary
Aspects of the disclosure relate to identifying potentially malicious messages and generating instream alerts based on real-time message monitoring. A computing platform may monitor a plurality of messages received by a messaging server associated with an operator. Subsequently, the computing platform may detect that a message of the plurality of messages is potentially malicious. In response to detecting that the message of the plurality of messages is potentially malicious, the computing platform may execute one or more protection actions. In executing the one or more protection actions, the computing platform may generate an alert message comprising information indicating that the message of the plurality of messages is potentially malicious. Then, the computing platform may send the alert message to the messaging server, which may cause the messaging server to deliver the alert message to a computing device associated with an intended recipient of the message.