IMPROVING WI-FI SECURITY AGAINST EVIL TWIN ATTACK USING LIGHT WEIGHT MACHINE LEARNING APPLICATION

  • Dr. Harsha S Jyothy Institute of Technology affiliated to Visvesvaraya Technological University
  • Dr. Khalid Nazim S A
  • Dr. S Balaji
  • Vallabh V Rao
Keywords: WiFi Security, Machine Learning, Evil Twin, MalNet, Bayesian Classification

Abstract

In the current world, all the devices are aiming to be or already are wireless and mobile. The trend is building smarter devices that offer the users all their required services with minimal human intervention. Due to this all manufacturers design their devices to be signal hungry as that is the only requirement that users feel. Since this has been the motto of all brands of wireless telecommunication devices to better the user experience, all devices attempt to automatically latch on to the network that has the highest signal strength and that is easily available. However, this design makes the devices vulnerable to a classic "MalNet[1]" attack called "Evil Twin[1]".  Most devices suffer data loss or bandwidth loss regularly to this attack. There are also cases of financial losses [3] suffered by the users because of the said attack. In this paper we have attempted to use Android API to build a simple light weight security system that can prevent the evil twin attack.

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Author Biography

Dr. Harsha S, Jyothy Institute of Technology affiliated to Visvesvaraya Technological University
Associate Professor, Department of Information Science & Engineering

References

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Published
2019-04-01
How to Cite
S, D. H., Nazim S A, D. K., Balaji, D. S., & Rao, V. V. (2019). IMPROVING WI-FI SECURITY AGAINST EVIL TWIN ATTACK USING LIGHT WEIGHT MACHINE LEARNING APPLICATION. COMPUSOFT: An International Journal of Advanced Computer Technology, 8(3). Retrieved from https://ijact.in/index.php/ijact/article/view/813