Machine Learning Model Population for Poisoning Attack Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing machine learning systems are vulnerable to 'poisoning' attacks where abnormal data is injected, compromising the integrity of training data and decision-making processes, and conventional approaches struggle to accurately detect and counteract such malfeasance due to reliance on single models and binary labeling methods.
Innovation Solution
A collaborative artificial intelligence system employing a population of machine learning models that adaptively monitors interactions, generates confidence scores for data integrity, and dynamically reconfigures model ensembles to detect and counteract abnormal data injection by altering the population of models, blocking suspicious interactions, and continuously updating in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single machine learning model is used for decisioning, then the system is simpler to operate, but the system becomes vulnerable to poisoning attacks and loses reliability
Solution Approach 1:
The patent divides a single machine learning model into a population of multiple machine learning models. Each model in the population processes interactions independently, and their collective decisions determine the final outcome. This segmentation prevents poisoning attacks because an attacker would need to compromise multiple models simultaneously rather than a single model.
Solution Approach 2:
The patent combines multiple machine learning models into a unified population that works together to make decisions. The models are trained on the same interaction data but maintain independent decision-making capabilities. Their combined output provides robustness against poisoning attacks while maintaining system coherence through shared training data and coordinated decision processes.
2Measurement precision
If conventional binary labeling methods are used, then the detection process is simpler, but the accuracy of detecting abnormal data injection is reduced
Solution Approach 1:
The patent implements a feedback mechanism where the population of machine learning models continuously monitors interaction data and provides feedback about abnormal patterns. When models detect potential poisoning attacks, they generate signals that trigger further analysis and model retraining. This feedback loop enables continuous improvement of detection accuracy without requiring complex manual intervention.
Solution Approach 2:
The patent transitions from static binary labeling to dynamic confidence scoring. Instead of fixed threshold-based classification, the system generates confidence scores that adaptively reflect the likelihood of abnormal data injection. This dynamic approach allows the system to adjust its detection sensitivity based on the current state of the interaction data and model performance.
3Adaptability or versatility
If the machine learning model population is continuously modified in real time, then the system's adaptability to detect poisoning attacks is improved, but the computational resources and system complexity increase
Solution Approach 1:
The patent enables the machine learning model population to self-adjust and self-repair in response to detected poisoning attacks. When abnormal data injection is identified, the system automatically retrains models using clean data from the population, removing compromised models, and redistributing workloads. This self-service capability maintains high adaptability without requiring constant external intervention or complex manual management.
Solution Approach 2:
The patent dynamically changes system parameters such as model population size, training data composition, and decision thresholds in response to detected threats. When poisoning attacks are identified, the system adjusts the population configuration by removing affected models and retraining others with modified parameters. These parameter changes enable real-time adaptability while maintaining manageable system complexity through automated control.
Data Source
AI summary
Embodiments of the present invention provide a system and methods to prevent poisoning attacks in machine learning systems in real time. The invention includes methods for blocking the injection of abnormal data into training data sets used to train machine learning models for the identification of malfeasant activity by blocking certain data from entering the machine learning training dataset in real time, blocking certain interactions from being completed in real time, or placing holds on certain resources or users according to patterns detected by the ensemble of machine learning models. Various thresholds may be set manually or identified through the machine learning algorithm in order to determine which interactions or users should be blocked.