Security of Wi-Fi as a Key Factor for IoT

Židková, Nikola; Maryska, Milos; Nedomova, Lea; Doucek, Petr

doi:10.36689/uhk/hed/2020-01-101

Cited by 4 publications

(4 citation statements)

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“…The main idea of the k-means algorithm is to represent each cluster by its average value, i.e. the centroid, and to minimize the objective function (1). For this…”

Section: Division Of Rssi Values Into 2 Clustersmentioning

confidence: 99%

“…WLANs are becoming more and more vulnerable to dangerous attacks. One of the most serious threats to WLAN security in recent years is rogue access points, which violators use for espionage and attacks [1]. An open wireless transmission medium allows an attacker to easily detect the MAC addresses of other devices, which are usually used as unique identifiers for all nodes on the network.…”

Section: Introductionmentioning

confidence: 99%

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Received-Signal-Strength-Based Approach for Detection and 2D Indoor Localization of Evil Twin Rogue Access Point in 802.11

Korolkov¹,

Kutsak²

2021

IJSSE

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The “Evil twin” rogue access point is one of the most serious security threats to wireless LANs. To solve this problem, a practical approach has been proposed for detecting rogue access points using the received signal strength indicator (RSSI). First, a distributed architecture is presented, which consists of three network analyzers. Then, a cluster analysis of the RSSI vectors is performed to determine the attack. The coordinates of the centroids of clusters obtained were converted into the distance by using an empirical model of signal propagation under indoor conditions. The obtained distances are used to determine the localization of a rogue access point (RAP) using the trilateration method. Finally, we are conducting experiments to evaluate the performance of practical RAP detection. The results show that the proposed approach to detecting rogue access points can significantly reduce the frequency of false alarms, while providing an average localization error of 1.5m, which is quite acceptable for RAP localization in real indoor conditions.

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“…The main idea of the k-means algorithm is to represent each cluster by its average value, i.e. the centroid, and to minimize the objective function (1). For this…”

Section: Division Of Rssi Values Into 2 Clustersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Received-Signal-Strength-Based Approach for Detection and 2D Indoor Localization of Evil Twin Rogue Access Point in 802.11

Korolkov¹,

Kutsak²

2021

IJSSE

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“…When these devices try to connect, there will be difficulties incompatibility for information exchange. In healthcare, power consumption and emitted radiation from IoT devices must be maintained as low as possible [14]. For different standards, a smooth transition in terms of speed and bandwidth is always challenging to maintain.…”

Section: ) Compatibilitymentioning

confidence: 99%

“…This calls for well-informed protective behaviour. The aim is to evaluate and prepare for eventual future situations and defects to improve safety arrangements and identify and respond to them [14].…”

Section: ) Ebiosmentioning

confidence: 99%

A Conceptual Model for Internet of Things Risk Assessment in Healthcare Domain with Deep Learning Approach

et al. 2020

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The Internet of Things (IoT) has become a prevalent technology in the IT industry. One of the industries that can benefit extensively in this technology is healthcare. However, the healthcare IoT is still under debate with several studies suggesting it is lack of interoperability, security, and too much complexity. Even more, the risk involved in deploying it is still enormous. Many traditional risk assessment models are unable to provide a specific IoT risk guideline and specification, especially in the healthcare area. Thus, it is essential to understand the full extent of the IoT risk and how to manage its risk in the healthcare area. The risk management models, such as NIST SP 800-30, ISO/IEC 27005, OCTAVE, CRAMM, and EBIOS, which are among the leading and widely used in many areas and healthcare fields, have also been described. Besides, this paper includes a review of three IoT risk assessment models that are based on ABA-IDS, Deep Learning, and AHP-SVM. Based on the review analysis, we proposed a new enhanced healthcare IoT risk assessment model, which aims to provide a real-time monitoring and mitigating risks that incorporate the NIST SP 800-30 framework, ABA-IDS, and CNN deep learning. This shall constitute a better classification of each risk identified to find the best risk mitigation plan.

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