ML Model Attack Detection via Historical Baseline Comparison
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Solution Overview
Problem
Current communication systems face challenges in detecting and mitigating data poisoning attacks on machine learning models, which can compromise the integrity and reliability of analytics information.
Innovation Solution
An apparatus and method that involve receiving a request for analytics information from a first machine learning model, obtaining a second machine learning model trained prior to the first model, and determining if the first model has been attacked by analyzing concept drift between the two models' inference outputs, with the option to provide feedback and identify the affected network function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are used to provide analytics information in communication systems, then the system can improve its ability to detect and respond to network conditions, but the models become vulnerable to data poisoning attacks that compromise integrity and reliability
Solution Approach 1:
The system performs preliminary actions by obtaining and storing a second machine learning model that was trained prior to the first model. This historical model serves as a baseline for future attack detection, allowing the system to preemptively establish reference points before poisoning attacks occur. The preliminary training and storage of the second model enables subsequent comparison to detect concept drift indicating poisoning attempts.
Solution Approach 2:
The system implements feedback mechanisms by comparing the first inference output from the potentially poisoned model against the second inference output from the historical model. This feedback loop detects concept drift between the two models, providing early warning signals when a model has been compromised. The feedback enables the system to identify attacks and trigger mitigation procedures.
2Difficulty of detecting and measuring
If the system compares inference outputs from two machine learning models to detect attacks, then attack detection capability improves, but system complexity increases due to additional model management and comparison operations
Solution Approach 1:
The system achieves multi-functionality by using the same machine learning model infrastructure for both normal analytics operations and attack detection. The historical second model serves dual purposes: it can be used for analytics if the first model is compromised, and it provides the baseline for detecting concept drift. This universal approach reduces the need for separate dedicated detection systems.
Solution Approach 2:
The system creates a copy of the historical second machine learning model for comparison purposes. This copy enables the system to detect attacks by comparing inference outputs without requiring the original historical model to be modified or stored in a separate complex structure. The copying approach simplifies the detection mechanism while maintaining detection accuracy.
3Measurement precision
If the system obtains and stores historical machine learning models for comparison, then attack detection accuracy improves, but storage requirements and data management complexity increase
Solution Approach 1:
The system extracts only the necessary components for attack detection from the historical second model - specifically the model parameters and architecture needed for inference output generation. By extracting only these essential elements rather than storing complete model artifacts, the system reduces storage requirements while maintaining detection accuracy. The extracted model components are sufficient for comparison without requiring full model repos.
Data Source
AI summary
There is provided an apparatus comprising means for: receiving a request from an analytics consumer for analytics information from a first machine learning model, obtaining the first machine learning model, obtaining a second machine learning model, the second machine learning model being trained prior to the first machine learning model, the first machine learning model and the second machine learning model having the same analytics identifier; obtaining a first inference output from the first machine learning model and a second inference output from the second machine learning model, determining, based on the first inference output and the second inference output that the first machine learning model has been attacked and providing an indication to a network entity that the first machine learning model has been attacked.