JIPS (한국정보처리학회)
Current Result Document :
| 한글제목(Korean Title) |
Enhancing the Reliability of Wi-Fi Network Using Evil Twin AP Detection Method Based on Machine Learning |
| 영문제목(English Title) |
Enhancing the Reliability of Wi-Fi Network Using Evil Twin AP Detection Method Based on Machine Learning |
| 저자(Author) |
Jeonghoon Seo
Chaeho Cho
Yoojae Won
|
| 원문수록처(Citation) |
VOL 16 NO. 03 PP. 0541 ~ 0556 (2020. 06) |
한글내용
(Korean Abstract) |
|
영문내용
(English 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 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.
|
| 키워드(Keyword) |
Access Point
Classification Algorithm
Clock Skew
Evil Twin AP
Rogue AP
Wireless Network
|
| 파일첨부 |
PDF 다운로드
|
