IMPROVING WI-FI SECURITY AGAINST EVIL TWIN ATTACK USING LIGHT WEIGHT MACHINE LEARNING APPLICATION
AbstractIn 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" attack called "Evil Twin".Â Most devices suffer data loss or bandwidth loss regularly to this attack. There are also cases of financial losses  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.
. Szongott C., Henne B., Smith M. (2012) Mobile Evil Twin Malnets â€“ 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, â€œBayesian spatial binary classificationâ€, Spatial Statistics, Volume 16, 2016, Pages 72-102, ISSN 2211-6753, https://doi.org/10.1016/j.spasta.2016.01.004
. 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)
. 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.
. 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.
The submitter hereby warrants that the Work (collectively, the “Materials”) is original and that he/she is the author of the Materials. To the extent the Materials incorporate text passages, figures, data or other material from the works of others, the undersigned has obtained any necessary permissions. Where necessary, the undersigned has obtained all third party permissions and consents to grant the license above and has all copies of such permissions and consents.
The submitter represents that he/she has the power and authority to make and execute this assignment. The submitter agrees to indemnify and hold harmless the COMPUSOFT from any damage or expense that may arise in the event of a breach of any of the warranties set forth above. For authenticity, validity and originality of the research paper the author/authors will be totally responsible.