Privileged User Transaction Anomaly Detection via Behavioral Modeling
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Solution Overview
Problem
Current application performance monitoring systems rely on instrumentation that assumes built-in security mechanisms, which are inadequate to prevent privileged user abuse, such as data mining and exfiltration actions, as they do not effectively differentiate between legitimate and malicious activities.
Innovation Solution
Implementing a device that captures transaction data from privileged user accounts using instrumentation code, making inferences about the data through a behavioral model, and enforcing mitigation actions based on an enforcement policy to prevent anomalous transactions within the application.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If instrumentation code is inserted into the application to capture performance data, then performance monitoring capability is improved, but the application cannot differentiate between legitimate and malicious transactions by privileged users
Solution Approach 1:
A behavioral model is introduced as an intermediary between the instrumentation code and the security enforcement mechanism. The behavioral model analyzes transaction patterns and user behaviors to distinguish between legitimate and malicious activities, providing a bridge between data capture and security decision-making without requiring changes to the core application code
Solution Approach 2:
The system changes the parameters of transaction evaluation by incorporating behavioral analysis dimensions such as transaction frequency, time patterns, data access patterns, and user interaction sequences. These parameter changes enable the system to detect anomalies that traditional binary security checks cannot identify
2Ease of operation
If built-in security mechanisms are used to control user access, then authentication is simplified, but the system cannot prevent abuse of privileges within the application
Solution Approach 1:
The system implements continuous feedback loops where transaction data is constantly monitored, analyzed against behavioral models, and used to dynamically adjust security decisions. This feedback mechanism enables the system to respond to emerging threats while maintaining simple authentication operations, as the complexity is shifted to the monitoring and analysis layer rather than the authentication layer
3Speed
If traditional binary security checks are performed, then privilege verification is fast, but anomalous transactions by authorized users cannot be detected
Solution Approach 1:
The system performs preliminary actions by pre-establishing behavioral baselines and anomaly detection rules before actual transactions occur. The behavioral model is trained and configured in advance, allowing the system to quickly compare actual transactions against predefined patterns without performing complex analysis in real-time, thus maintaining fast verification speed while improving detection precision
Data Source
AI summary
In one embodiment, a device obtains data regarding a transaction attempted by a user account within an online application that is captured by instrumentation code that is inserted into the online application at runtime, wherein the user account has sufficient privileges within the online application to perform the transaction. The device makes an inference about the data regarding the transaction using a behavioral model. The device determines, based on the inference, a mitigation action for performance within the online application according to an enforcement policy. The device enforces the mitigation action within the online application.