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Sample Size Calculation01:19

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Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
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Dynamic sample size detection in learning command line sequence for continuous authentication.

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  • 1Electrical and Computer Engineering Department, University of Victoria, Victoria, BC V8W 3P6, Canada. itraore@ece.uvic.ca

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Summary
This summary is machine-generated.

Continuous authentication (CA) uses sequential sampling to balance detection accuracy and delay. This method effectively combats session hijacking by optimizing the trade-off between detecting threats quickly and minimizing false rejections.

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Machine Learning

Background:

  • Continuous authentication (CA) aims to prevent session hijacking by frequent user verification.
  • Balancing detection accuracy and delay is crucial for effective CA systems.
  • High accuracy and short detection delays present conflicting requirements.

Purpose of the Study:

  • To propose a sequential sampling technique for optimizing continuous authentication.
  • To achieve a balance between detection delay and accuracy in CA.
  • To mitigate session hijacking risks through improved authentication methods.

Main Methods:

  • Utilized sequential sampling for continuous authentication.
  • Implemented CA based on user command line sequences.
  • Employed a naïve Bayes classification scheme for detection.

Main Results:

  • On the Greenberg dataset: False Acceptance Rate (FAR) = 11.78%, False Rejection Rate (FRR) = 1.33%, average detection delay = 37 commands.
  • On the Schonlau (SEA) dataset: FAR = 4.28%, FRR = 12%.

Conclusions:

  • Sequential sampling offers an effective approach to optimize continuous authentication.
  • The proposed method successfully balances detection delay and accuracy.
  • This technique shows promise in enhancing security against session hijacking attacks.