• Journal of Internet Computing and Services
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• v.17 no.3
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• pp.11-18
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• 2016

Accessing unauthorized rogue APs in WiFi environments is a very dangerous behavior which may lead WiFi users to be exposed to the various cyber attacks such as sniffing, phishing, and pharming attacks. Therefore, prompt and precise detection of rogue APs and properly alarming to the corresponding users has become one of most essential requirements for the WiFi security. This paper proposes a new rogue AP detection method which is mainly using the installation information of authorized APs and the DHCP snooping information of the corresponding switches. The proposed method detects rogue APs promptly and precisely, and notify in realtime to the corresponding users. Since the proposed method is simple and does not require any special devices, it is very cost-effective comparing to the wireless intrusion prevention systems which are normally based on a number of detection sensors and servers. And it is highly precise and prompt in rogue AP detection and flexible in deployment comparing to the existing rogue AP detection methods based on the timing information, location information, and white list information.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.3
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• pp.431-444
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• 2013

Widespread use of smartphones and wireless LAN accompany a threat called rogue AP. When a user connects to a rogue AP, the rogue AP can mount the man-in-the-middle attack against the user, so it can easily acquire user's private information. Many researches have been conducted on how to detect a various kinds of rogue APs, and in this paper, we are going to propose an algorithm to identify and detect a rogue AP that impersonates a regular AP by showing a regular AP's SSID and connecting to a regular AP. User is deceived easily because the rogue AP's SSID looks the same as that of a regular AP. To detect this type of rogue APs, we use a machine learning algorithm called SVM(Support Vector Machine). Our algorithm detects rogue APs with more than 90% accuracy, and also adjusts automatically detection criteria. We show the performance of our algorithm by experiments.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.733-734
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• 2011

지난 몇 년 동안 무선랜(Wireless LAN)은 다양한 영역에서 가장 널리 사용 되었으며, 가장 크게 발전을 하였다. 그러나 무선랜의 특성상 해킹과 침투에 취약한 약점을 안고 있다. 아직도 많은 보안적 취약점을 가지고 있으며, 특히 그 중에서도 Rogue AP(Access Point)는 가장 심각한 보안 취약점으로 대두되고 있다. 현재 Rogue AP 탐지를 위하여 넷스텀블러와 같은 스니핑 소프트웨어를 설치하여 주변 지역을 돌아다니는 워드라이빙 형태의 탐지방법은 아직도 사용되고 있다. 그러나, 이러한 방법은 대규모로 확장되어 가는 무선랜 환경에 적합하지 않다. 본 논문은 무선랜 환경에서 Rogue AP 탐지 문제의 해결책을 제시한다. AP의 전파 영역을 이용하는 방식으로, AP가 신호를 받을 수 있도록 수정하여, 주변에 새로운 AP가 탐지될 경우, AP가 서버와 새롭게 발견된 AP에 신호를 보내고, 이를 바탕으로 서버는 WhiteList를 통해서 Rogue AP 여부를 결정한다. 따라서 본 논문의 제안 방식은 기존의 탐지 방식에 비해 Rogue AP의 효과적 탐지가 가능하다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.2
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• pp.334-348
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• 2015

Widespread use of smart devices accompanies increase of use of access point (AP), which enables the connection to the wireless network. If the appropriate security is not served when a user tries to connect the wireless network through an AP, various security problems can arise due to the rogue APs. In this paper, we are going to examine the threat by evil-twin, which is a kind of rogue APs. Most of recent researches for detecting rogue APs utilize the measured time difference, such as round trip time (RTT), between the evil-twin and authorized APs. These methods, however, suffer from the low detection rate in the network congestion. Due to these reasons, in this paper, we suggest a new factor, packet inter-arrival time (PIAT), in order to detect evil-twins. By using both RTT and PIAT as the learning factors for the support vector machine (SVM), we determine the non-linear metric to classify evil-twins and authorized APs. As a result, we can detect evil-twins with the probability of up to 96.5% and at least 89.75% even when the network is congested.

• 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.