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- W3207389635 abstract "In IEEE 802.11 standard, the management frames are sent unencrypted, so the network name (SSID), MAC address (BSSID), and/or IP address can be easily spoofed. Impersonating existing AP with faked one to steal sensitive information from the connected devices is known as an evil twin attack. The current approaches for detecting Evil-twin AP depends on techniques as clock skew, route option, IP packet header, and data frame statistics. The relevant literature approaches are either outdated, limited in their detection methods, architecture, and/or scope of detection. This research proposed an admin and user tool that can detect the evil twin attack. In this paper, we detect the de-authentication and disassociation packets or both (mixed frames), as it is an essential part of evil twin attack. By using a low-cost microcontroller capability to detect and classify frames and then trigger different lighting alert for each type of frames. The main contribution of this paper does not lie only in its ability to detect different types of attack but also in detecting them in real-time and determining the attacker’s MAC address. It is prototyped under real attack as it is implemented over two different scenarios, in both admin and user side then compared with other detection method. Experimental results show accuracy rate of 95.30% for the admin side in (DE authentication attack—disassociation attack—mixed attack—normal packets). While it proves accuracy rate of 88.18% for the user side." @default.
- W3207389635 created "2021-10-25" @default.
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- W3207389635 date "2021-08-24" @default.
- W3207389635 modified "2023-09-30" @default.
- W3207389635 title "Detecting Semantic Social Engineering Attack in the Context of Information Security" @default.
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- W3207389635 doi "https://doi.org/10.1007/978-981-16-2275-5_3" @default.
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