Adaptive Bot Detection via Iterative Classifier Evolution
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Solution Overview
Problem
Current bot detection systems, including ANN bot detection engines, are limited by their training sets and struggle to identify new bot variants and 'zero day' bots effectively, as they rely on previously discovered bot patterns, and are overwhelmed by the rapid creation and deployment of new bot variants.
Innovation Solution
An adaptive bot detection system utilizing an iterative feedback loop with a community of ANN engines that continuously train and evolve using CAPTCHA challenges to improve accuracy, replacing underperforming models and introducing randomness to detect new bot types, incorporating a master ANN engine and community engines that compete to become the most accurate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional bot detection systems use fixed training sets, then they can accurately detect known bot variants, but they fail to identify new bot variants and zero day bots
Solution Approach 1:
The system implements dynamic classifier evolution where machine learning classifiers are continuously retrained and updated in generations. Instead of using fixed training sets, the system adapts classifiers over time to detect emerging bot variants, transforming the static detection approach into a dynamic, evolving system that maintains reliability while improving adaptability.
Solution Approach 2:
The system incorporates feedback mechanisms where detection results from multiple classifiers are aggregated and used to identify patterns. The feedback loop allows the system to learn from misclassifications and successful detections, continuously improving its ability to detect both known and new bot variants while maintaining accuracy on established threats.
2Measurement precision
If machine learning classifiers are trained on large datasets, then detection precision improves, but training complexity and computational requirements increase
Solution Approach 1:
The system segments the training process into multiple generations of classifiers, each trained on specific subsets of data. Instead of training one complex classifier on all data, the system divides the workload across multiple simpler classifiers that are trained incrementally, reducing individual training complexity while maintaining overall detection precision through ensemble aggregation.
Solution Approach 2:
The system uses partial training approaches where classifiers are trained on representative subsets of data rather than complete datasets. By using sampling and incremental learning, the system achieves sufficient detection precision without the computational burden of training on all available data, balancing precision requirements with training feasibility.
Data Source
AI summary
A computer system is provided. The computer system includes a memory, a network interface, and a processor coupled to the memory and the network interface. The processor is configured to receive a response to a request to verify whether an ostensible client of a service is actually a client or a bot, the response including an indicator of whether the ostensible client is a client or a bot; receive information descriptive of interoperations between the ostensible client and the service that are indicative of whether the ostensible client is a client or a bot; and train a plurality of machine learning classifiers using the information and the indicator to generate a next generation of the plurality of machine learning classifiers.