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Behavioral Biometrics and Machine Learning to Secure Website Logins

  • Falaah Arif KhanEmail author
  • Sajin Kunhambu
  • K. Chakravarthy G
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 969)

Abstract

In a world dominated by e-commerce and electronic transactions, the business value of a secure website is immeasurable. With the ongoing wave of Artificial Intelligence and Big Data, hackers have far more sophisticated tools at their disposal to orchestrate identity fraud on login portals. Such attacks bypass static security rules and hence protection against them requires the use of machine learning based ‘intelligent’ security algorithms. This paper explores the use of client behavioral biometrics to secure website logins. A client’s mouse dynamics, keystrokes and click patterns during login are used to create a customized security model for each user that can differentiate the user of interest from any other impersonator. Such a model, combined with existing protocols, will provide enhanced security for the user’ profile, even if credentials are compromised. The module first employs a means of collecting relevant behavioral data from the client side when a new account is created. The collection module can easily be integrated with any web application without impacting website performance. After sufficient collection of login data, a biometric-based fraud detection algorithm is created that secures the account against future impersonators. Our choice of algorithms is the Multilayer Perceptron, Support Vector Machine and Adaptive Boosting, the outcomes of which are polled to give the prediction. We find that such a model shows good performance (accuracy, precision and recall) for different train: test splits. Moreover, the model is easily implementable for any web based authentication, is scalable and can be fully automated, if a dataset like ours can be created from client activity on the web application of interest.

Keywords

Behavioral biometrics Machine learning Artificial Intelligence Login fraud Intelligent security Keystroke Mouse movements Multilayer Perceptron Support vector machine Adaptive Boosting 

Notes

Acknowledgment

We thank our managers; Mukund and Swami for their unwavering support. We also extend a hearty thanks to all the interns at Dell, Hyderabad who took part in the process of data collection. Without the data, there could have been no machine learning and so your contribution does not go unnoticed. We dedicate this project to the Python community for all the extraordinary work they do in creating new useful libraries for developers, while maintaining requisite documentation and user support on existing libraries. The work of this study, like the work of countless others, would not have been possible without their unwavering dedication to the Pythonic way.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Falaah Arif Khan
    • 1
    Email author
  • Sajin Kunhambu
    • 2
  • K. Chakravarthy G
    • 2
  1. 1.DES India, DellBangaloreIndia
  2. 2.DCS DCP India, DellBangaloreIndia

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