Search results for "Early decision"

showing 2 items of 2 documents

A Quantum-Inspired Classifier for Early Web Bot Detection

2022

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 levera…

Settore INF/01 - InformaticaComputer Networks and Communicationsbot detectionData modelsTime series analysisearly decisionquantum-inspired computingTime measurementCorrelationCostsmultinomial classificationPredictive modelsbot detection; Correlation; Costs; Data models; early decision; multinomial classification; multivariate sequence classification; Predictive models; quantum-inspired computing; sequential classification; Task analysis; Time measurement; Time series analysis;multivariate sequence classificationTask analysisSafety Risk Reliability and Qualitybot detection; Correlation; Costs; Data models; early decision; multinomial classification; multivariate sequence classification; Predictive models; quantum-inspired computing; sequential classification; Task analysis; Time measurement; Time series analysissequential classification
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Efficient on-the-fly Web bot detection

2021

Abstract A large fraction of traffic on present-day Web servers is generated by bots — intelligent agents able to traverse the Web and execute various advanced tasks. Since bots’ activity may raise concerns about server security and performance, many studies have investigated traffic features discriminating bots from human visitors and developed methods for automated traffic classification. Very few previous works, however, aim at identifying bots on-the-fly, trying to classify active sessions as early as possible. This paper proposes a novel method for binary classification of streams of Web server requests in order to label each active session as “bot” or “human”. A machine learning appro…

Web serverInformation Systems and ManagementComputer scienceInternet robot02 engineering and technologyMachine learningcomputer.software_genreUsage dataManagement Information SystemsIntelligent agentEarly decision; Internet robot; Machine learning; Neural network; Real-time bot detection; Sequential analysis; Web botArtificial IntelligenceReal-time bot detection020204 information systemsMachine learning0202 electrical engineering electronic engineering information engineeringFalse positive paradoxSequential analysisSession (computer science)business.industryWeb botNeural networkEarly decisionTraffic classificationBinary classification020201 artificial intelligence & image processingArtificial intelligencebusinesscomputerClassifier (UML)SoftwareKnowledge-Based Systems
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