MC-VAP: A multi-connection virtual access point for high performance software-defined wireless networks

Xu, Chuan; Jin, Wenqiang; Wang, Xinheng; Zhao, Guofeng; Yu, Shui

doi:10.1016/j.jnca.2018.08.009

Cited by 4 publications

(3 citation statements)

References 26 publications

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“…ONSIDERING THE increase in the number of devices connected to the Internet and the lack of the possibility of connecting to the Internet via cable on all of these devices, the use of wireless access networks such as WiFi has emerged as a practical solution [1]. In particular, Internet of Things (IoT), the way in which the interaction of human and technology changes a little more.…”

Section: Introductionmentioning

confidence: 99%

Automated Fake Access Point Attack Detection and Prevention System with IoT Devices

Kilinçer

Ertam

Şengür

2020

Balkan Journal of Electrical and Computer Engineering

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Wireless access points (APs), which allow many devices to be easily connected to the Internet, are widely used today because they offer the easiest way to connect to the Internet. With the development of the Internet of Things (IoT), WiFi networks are widely used in our homes, workplaces, social areas, campus areas. With the increase of WiFi networks, attacks on these networks are constantly increasing. In this study, an IoT-based approach to detect and prevent Fake Access point attacks frequently seen in WiFi networks is proposed. A Single Board Computer (SBC) and a wireless antenna in the "Soft AP" feature are used for operation. Fake APs were detected by air scanning. In the first phase of the study, fake Access point broadcasts have been created which can create security weakness. In order to determine the fake Access points created in the second stage, SBC and wifi module were used to scan air. In the final stage, the mac address of the fake AP has been assigned to an unauthorized Virtual Local Area Network (vLAN) on the network to prevent detected fake AP broadcasts. The possible attack methods for the study were implemented and it was revealed that the proposed approach prevented the attack successfully in all scenarios. The study is seen as an effective, developed and economically useful IoT application for network administrators to prevent the attack using fake Access point.

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Section: Introductionmentioning

confidence: 99%

Automated Fake Access Point Attack Detection and Prevention System with IoT Devices

Kilinçer

Ertam

Şengür

2020

Balkan Journal of Electrical and Computer Engineering

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“…1,2 In dense deployment wireless sense, both the interference from adjacent APs and the energy waste caused by a large number of idle APs are the pivotal problem need to be solved urgently. 1,2 In dense deployment wireless sense, both the interference from adjacent APs and the energy waste caused by a large number of idle APs are the pivotal problem need to be solved urgently.…”

Section: Introductionmentioning

confidence: 99%

“…The prevalence of Internet of things (IoT) has led to high density WiFi access points (APs). 1,2 In dense deployment wireless sense, both the interference from adjacent APs and the energy waste caused by a large number of idle APs are the pivotal problem need to be solved urgently. 3,4 Generally, the interference decreases the user performance, 5 such as throughput degradation and packet loss.…”

Section: Introductionmentioning

confidence: 99%

Modelling the impact of interference on the energy efficiency of WLANs

Han

Wang

et al. 2019

Concurrency and Computation

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The high-bandwidth demands from variety of applications drive a dense wireless local access network (WLAN), which results in a complicated wireless network scene with serious co-channel interference and energy waste. In this paper, to reveal the interactions between interference and energy efficiency, we propose an interference-energy efficiency (IFEE) model to quantify the interference impact on the energy efficiency of 802.11 access point (AP) devices. Firstly, we introduce the channel separation and the difference of received signal strength indication (D-RSSI) as two indicators to extend the classical signal to interference plus noise ratio (SINR) notion and rate adaptive mechanism. Then, these two parameters are integrated into the energy consumption model to establish the IFEE model. Lastly, we conduct extensive measurements with five typical WiFi interference scene in real network to validate the effectiveness of our model. The comparisons between simulation results and real data demonstrate that the proposed IFEE model can quantify the interference and energy efficiency with high accuracy, which can be used for wireless network optimization and protocol design.

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