Bot Detection via Behavioral Sequence Analysis
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Solution Overview
Problem
Existing systems fail to effectively detect and differentiate between human and bot traffic on network resources, leading to server overload and potential crashes due to malicious bot activities like botnets.
Innovation Solution
A system that constructs historical request sequences to evaluate new visits based on multiple scores, using techniques such as Kullback-Leibler divergence, principal component analysis, clustering, and deep learning to identify bot-like behavior and construct botnets, and employs a neural network to classify requests as human or bot.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional bot detection methods are used, then simple bot identification is possible, but botnets with human-like behavior cannot be effectively detected
Solution Approach 1:
The system dynamically adapts to evolving botnet behaviors by continuously learning from historical request sequences and updating its detection models. Instead of using static detection rules, the system employs machine learning algorithms that evolve alongside botnet tactics, enabling it to detect sophisticated botnets that mimic human-like browsing patterns.
Solution Approach 2:
The system transitions from analyzing single isolated requests to examining sequences of requests across multiple dimensions (temporal patterns, navigation paths, interaction behaviors). By constructing detection models based on request sequences rather than individual requests, the system adds dimensional depth to bot detection, enabling identification of coordinated botnet activities that operate within normal human-like parameters.
2Productivity
If all requests are processed individually, then simple request analysis is possible, but coordinated botnet behavior cannot be identified
Solution Approach 1:
The system segments request analysis into meaningful units (sequences of requests representing user sessions or task flows) rather than processing isolated requests. By dividing the analysis into sequences that capture behavioral patterns, the system maintains processing efficiency while enabling detection of coordinated botnet activities that manifest as abnormal patterns across multiple requests.
Solution Approach 2:
The system performs preliminary construction of detection models using historical request sequence data before actual bot detection occurs. By pre-processing and analyzing historical patterns to establish baseline behaviors and anomaly detection thresholds, the system prepares detection mechanisms in advance, enabling rapid real-time detection without sacrificing analysis depth.
3Ease of operation
If bot detection is not implemented, then all traffic is processed normally, but server load increases and performance degrades
Solution Approach 1:
The system introduces an intermediary detection layer between incoming requests and server processing. This intermediary analyzes request sequences and identifies bot traffic patterns before requests reach the server, filtering out malicious botnet requests while allowing legitimate human traffic to pass through unchanged. This maintains operational simplicity for legitimate users while protecting server performance.
4Measurement precision
If complex analysis methods are used, then accurate bot identification is possible, but computational overhead increases
Solution Approach 1:
The system applies partial analysis to individual requests while using excessive/comprehensive analysis on historical sequences to build detection models. By performing lighter-weight analysis on real-time requests against pre-computed models derived from comprehensive historical analysis, the system achieves high detection accuracy without incurring full computational overhead for every incoming request.
Data Source
AI summary
A system automatically detects bots and/or botnets.