Improving wi-fi security against evil twin attack using light weight machine learning application

Authors

  • S Harsha Associate Professor, Department of IS and E, Jyothy Institute of Technology, Bengaluru, Karnataka, India
  • SA Khalid Nazim Assistant Professor, Department of CSI, College of Science, Majmaah University, Al- Majmaah 11952, Saudi Arabia
  • Balaji S Professor, Computer Science and engineering, CIIRC, Jyothy Institute of Technology, Bengaluru, Karnataka, India
  • Rao VV Department of IS and E, Jyothy Institute of Technology, Bengaluru, Karnataka, India

Keywords:

Wi-Fi 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 pr

References

. Szongott C., Henne B., Smith M. (2012) Mobile Evil Twin Malnets – The Worst of Both Worlds. In: Pieprzyk J., Sadeghi AR., Manulis M. (eds) Cryptology and Network Security. CANS 2012. Lecture Notes in Computer Science, vol 7712. Springer, Berlin, Heidelberg

. Candace Berrett, Catherine A. Calder, “Bayesian spatial binary classification”, Spatial Statistics, Volume 16, 2016, Pages 72-102, ISSN 2211- 6753, https://doi.org/10.1016/j.spasta.2016.01.004 (http://www.sciencedirect.com/science/article/pii/S2211675316000063)

. Kavita Sharma, B.B. Gupta, Multi-layer Defense Against Malware Attacks on Smartphone Wi-Fi Access Channel, Procedia Computer Science, Volume 78, 2016, Pages 19-25, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2016.02.005. (http://www.sciencedirect.com/science/article/pii/S1877050916000077)

. https://www.cisco.com/c/dam/en/us/products/collateral/wireless/aironet-3600-series/white-paperc11-713103.pdf, 2015.

. https://usa.kaspersky.com/resourcecenter/preemptive-safety/public-wifi, 2015.

. A Survey of Android Security Threats and Defenses, Bahman Rashidi, Carol Fung, Journal of Wireless Mobile Networks, Ubiquitous

Computing, and Dependable Applications, volume: 6, number: 3, pp. 3-35, 2015.

. William Enck, Damien Octeau, Patrick McDaniel, and Swarat Chaudhuri. 2011. A study of android application security. In Proceedings of the 20th USENIX conference on Security (SEC'11). USENIX Association, Berkeley, CA, USA, 21-21.

. Google work for Android Security White paper, 2015.

. K. Bauer, H. Gonzales and D. McCoy, "Mitigating Evil Twin Attacks in 802.11," 2008 IEEE International Performance, Computing and

Communications Conference, Austin, Texas, 2008, pp. 513-516. doi: 10.1109/PCCC.2008.4745081

. Vibhawari V. Nanavare, Prof. Dr. V. R. Ghorpade, Robust and Effective Evil Twin Access Point Detection Technique at End User Side, International Journal of Innovative Research in Science, Engineering and Technology, ISSN online: 2319-8753, ISSN Print: 2347-6710.

. Dr. Adiline Macriga G., Prevention Technique for Creating Fake Profiles and Accounts on Websites, COMPUSOFT, An International

Journal of Advanced Computer Technology, 2018, ISSN: 2320-0790, pp. 2826-2830.

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Published

2024-02-26

How to Cite

S, H., S A , K. N., Balaji, S., & Rao, V. V. (2024). Improving wi-fi security against evil twin attack using light weight machine learning application. COMPUSOFT: An International Journal of Advanced Computer Technology, 8(03), 3075–3078. Retrieved from https://ijact.in/index.php/j/article/view/483

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Section

Original Research Article

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