Messaging Account Anomaly Detection Using Autoencoder Semantics
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Solution Overview
Problem
Existing messaging systems face challenges in efficiently detecting and mitigating account takeovers, which are labor-intensive and resource-consuming, leading to delays and increased latency due to manual identification and remediation processes.
Innovation Solution
Implementing a neural network autoencoder trained on a customer's messaging data to automatically filter and verify outgoing messages by analyzing semantic features, using an encoder to reduce dimensionality and a decoder to restore the message representation, enabling automated detection of anomalous activity and triggering remedial actions based on predefined thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual identification of compromised accounts is used, then accuracy in detecting account takeover can be maintained, but labor intensity and time consumption increase significantly
Solution Approach 1:
The system performs self-service by automatically detecting and responding to account takeover attempts using machine learning models and automated remedial actions, eliminating the need for manual security analyst intervention and reducing both time consumption and labor intensity while maintaining detection accuracy
Solution Approach 2:
The patent replaces manual mechanical inspection processes with automated electronic systems that use machine learning algorithms to analyze messaging patterns, detect anomalies, and execute remedial actions, thereby reducing time consumption while maintaining or improving detection accuracy
2Speed
If manual monitoring of messaging activity is implemented, then resource consumption can be minimized, but detection speed and responsiveness decrease
Solution Approach 1:
The system continuously monitors messaging activity in real-time using automated machine learning models, maintaining constant surveillance without human intervention, which enables immediate detection of anomalies and rapid response to account takeover attempts while optimizing resource utilization through efficient automated processing
3Loss of time
If automated remedial actions are implemented, then response time to account takeover events improves, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-configuring automated remedial actions and thresholds based on machine learning models, so that when account takeover attempts are detected, the system can immediately execute pre-planned responses without requiring complex real-time decision-making, thereby reducing response time while managing system complexity through preparation
Data Source
AI summary
Systems and methods for messaging system account management can include receiving an electronic message is originated by a customer account for transmission to a recipient and determining an encoded representation of the electronic message by inputting a numeric representation of the electronic message into an autoencoder. They can further include determining a value reflecting a difference between the encoded representation of the electronic message and encoded representations of the previous messages originated by the customer account and responsive to determining that the value reflecting the difference satisfies a predefined condition, transmitting the electronic message to a recipient.