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A Quantum-Inspired Classifier for Early Web Bot Detection
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DOI：10.1109/TIFS.2022.3170237.png)
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摘要
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This paper introduces a novel approach, inspired by the principles of Quantum Computing, to address web bot detection in terms of real-time classification of an incoming data stream of HTTP request headers, in order to ensure the shortest decision time with the highest accuracy. The proposed approach exploits the analogy between the intrinsic correlation of two or more particles and the dependence of each HTTP request on the preceding ones. Starting from the a-posteriori probability of each request to belong to a particular class, it is possible to assign a Qubit state representing a combination of the aforementioned probabilities for all available observations of the time series. By leveraging the underlying mathematical details of superposition and entanglement on specific subsequences, it is possible to devise a measure of membership to each class, thus enabling the system to take a reliable decision when a sufficient level of confidence is met or to continue with additional observations. The results reported in this paper objectively show the effectiveness of our quantum-inspired algorithm which outperforms other state-of-the-art approaches, including our own one based on the Sequential Probability Ratio Test.
Keyword:
Time series analysis
Task analysis
Data models
Costs
Time measurement
Predictive models
Correlation
Quantum-inspired computing
bot detection
sequential classification
early decision
multinomial classification
multivariate sequence classification
期刊
IF:
8
论文数:
5.3K
被引数:
2.3W
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