Data Mining Approach for Evil Twin Attack Identification in Wi-Fi Networks

Roman Banakh, Elena Nyemkova (), Connie Justice, Andrian Piskozub and Yuriy Lakh
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Roman Banakh: Department of Information Technology Security, Lviv Polytechnic National University, 79013 Lviv, Ukraine
Elena Nyemkova: Department of Information Technology Security, Lviv Polytechnic National University, 79013 Lviv, Ukraine
Connie Justice: Purdue School of Engineering and Technology, Indiana University–Purdue University Indianapolis, Indianapolis, IN 46202, USA
Andrian Piskozub: Department of Information Technology Security, Lviv Polytechnic National University, 79013 Lviv, Ukraine
Yuriy Lakh: Department of Information Technology Security, Lviv Polytechnic National University, 79013 Lviv, Ukraine

Data, 2024, vol. 9, issue 10, 1-19

Abstract: Recent cyber security solutions for wireless networks during internet open access have become critically important for personal data security. The newest WPA3 network security protocol has been used to maximize this protection; however, attackers can use an Evil Twin attack to replace a legitimate access point. The article is devoted to solving the problem of intrusion detection at the OSI model’s physical layers. To solve this, a hardware–software complex has been developed to collect information about the signal strength from Wi-Fi access points using wireless sensor networks. The collected data were supplemented with a generative algorithm considering all possible combinations of signal strength. The k-nearest neighbor model was trained on the obtained data to distinguish the signal strength of legitimate from illegitimate access points. To verify the authenticity of the data, an Evil Twin attack was physically simulated, and a machine learning model analyzed the data from the sensors. As a result, the Evil Twin attack was successfully identified based on the signal strength in the radio spectrum. The proposed model can be used in open access points as well as in large corporate and home Wi-Fi networks to detect intrusions aimed at substituting devices in the radio spectrum where IEEE 802.11 networking equipment operates.

Keywords: data; machine learning model; KNN; generative algorithm; intrusion detection; IEEE 802.11; Evil Twin attack (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2024
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