• Journal of Information Processing Systems
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• v.16 no.3
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• pp.541-556
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• 2020

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 impersonated by mimicking its service set identifier (SSID) and media access control (MAC) address. Evil twin APs are a major source of deception in wireless networks, facilitating message forgery and eavesdropping. Hence, it is necessary to detect them rapidly. To this end, numerous methods using clock skew have been proposed for evil twin AP detection. However, clock skew is difficult to calculate precisely because wireless networks are vulnerable to noise. This paper proposes an evil twin AP detection method that uses a multiple-feature-based machine 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 method has an evil twin AP detection accuracy of 100% using the random forest algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.262-265
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• 2015

In modern society, the spread of computers and smart phones, the Internet has become part of life. Therefore, the most places, wired/wireless router is installed. Crackers can attack the user to use the router by exploiting network vulnerabilities. therefore, the administrator is able to try cracker variety of attacks sloppy router, vaccine is installed also computer and smartphone users, appearance and address of the usual Internet is not deer medium and similar. In this paper, we look at a method for preventing in each attack seen for typical network attacks.