Beyond KNN: Deep Neighborhood Learning for WiFi-based Indoor Positioning Systems

Dong, Yinhuan; Zampella, Francisco; Alsehly, Firas

doi:10.1109/wcnc55385.2023.10118752

Cited by 1 publication

(1 citation statement)

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“…When several small packets are transmitted separately, each packet needs its own header and other control information. These overheads can quickly accumulate and take up a significant portion of the available bandwidth, leaving Work [13] proposes an approach for determining the positioning of nodes in Wi-Fi networks based on the k-nearest neighbors (KNN) machine learning algorithm. Although the main goal of the considered work is not to improve the state of cyber security in wireless computer networks, nevertheless, the application of machine learning algorithms can provide advantages in solving some related problems.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Devising a method for detecting “evil twin” attacks on IEEE 802.11 networks (Wi-Fi) with KNN classification model

Banakh,

Piskozub,

Opirskyy

2023

EEJET

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The object of research is IEEE 802.11 (Wi-Fi) networks, which are often the targets of a group of attacks called "evil twin". Research into this area is extremely important because Wi-Fi technology is a very common method of connecting to a network and is usually the first target of cybercriminals when they attack businesses. With the help of a systematic analysis of the literature focused on countering attacks of the "evil twin" type, this work identifies the main advantages of using artificial intelligence systems in the analysis of network data and identification of intrusions in Wi-Fi networks. To evaluate the effectiveness of intrusion detection and cybercrime analysis, a number of experiments as close as possible to real attacks on Wi-Fi networks were conducted. As part of the research reported in this paper, a method is proposed for detecting cybercrimes in IEEE 802.11 (Wi-Fi) wireless networks using artificial intelligence, namely a model built on the basis of the k-nearest neighbors method. This method is based on the classification of previously collected data, namely the signal strength from the access point, and then continuous comparison of the newly collected data with the trained model. A compact and energy-efficient prototype of a hardware and software system has been designed for the implementation of monitoring, analysis of ethernet network packets and data storage based on time series. In order to reduce the load on the computer network and taking into account the limited computing power of the system, a method of data aggregation was proposed, which ensures fast transfer of information. The results, namely 100 % of test cases (more than 7 thousand), were classified correctly, which indicates that the chosen method of data analysis will significantly increase the security of information and communication systems at the state and private levels

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Section: Literature Review and Problem Statementmentioning

confidence: 99%