Enhancing the Reliability of Wi-Fi Network Using Evil Twin AP Detection Method Based on Machine Learning
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초록
Wireless networks have become integral to society as they provide mobility and scalability advantages. However,their disadvantage is that they cannot control the media, which makes them vulnerable to various types of attacks. One example of such attacks is the evil twin access point (AP) attack, in which an authorized AP is impersonatedby mimicking its service set identifier (SSID) and media access control (MAC) address. Evil twin APs are amajor source of deception in wireless networks, facilitating message forgery and eavesdropping. Hence, it isnecessary to detect them rapidly. To this end, numerous methods using clock skew have been proposed for eviltwin AP detection. However, clock skew is difficult to calculate precisely because wireless networks arevulnerable to noise. This paper proposes an evil twin AP detection method that uses a multiple-feature-basedmachine learning classification algorithm. The features used in the proposed method are clock skew, channel,received signal strength, and duration. The results of experiments conducted indicate that the proposed methodhas an evil twin AP detection accuracy of 100% using the random forest algorithm.
Abstract
Wireless networks have become integral to society as they provide mobility and scalability advantages. However,their disadvantage is that they cannot control the media, which makes them vulnerable to various types of attacks. One example of such attacks is the evil twin access point (AP) attack, in which an authorized AP is impersonatedby mimicking its service set identifier (SSID) and media access control (MAC) address. Evil twin APs are amajor source of deception in wireless networks, facilitating message forgery and eavesdropping. Hence, it isnecessary to detect them rapidly. To this end, numerous methods using clock skew have been proposed for eviltwin AP detection. However, clock skew is difficult to calculate precisely because wireless networks arevulnerable to noise. This paper proposes an evil twin AP detection method that uses a multiple-feature-basedmachine learning classification algorithm. The features used in the proposed method are clock skew, channel,received signal strength, and duration. The results of experiments conducted indicate that the proposed methodhas an evil twin AP detection accuracy of 100% using the random forest algorithm.
참고문헌 (19)
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