Bluetooth Recurrent Application

Bluetooth Recurrent Application – The relationship between self-reported nocturnal sleep and single-task performance in healthy young adults: a machine learning study.

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Bluetooth Recurrent Application

Bluetooth Recurrent Application

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By Muhammad Zubair Muhammad Zubair Skillet Preprints.org Google Scholar 1, 2, *, ‡ , Ali Ghabish Ali Ghabish Skillet Preprints.org Google Scholar 3, Durim Anal Durim Anal Skillet Preprints.org Google Scholar ‡ Abdullah Abdullah, 1, Al Ali Scilit Preprints.org Google Scholar 2, Thomas Riemann Thomas Riemann Scilit Preprints.org Google Scholar 4, 5, Guillaume Alinier Guillaume Alinier Scilit Preprints.org Google Scholar 6, 7, 8, 9, Mohammad Hammoud Print Mohammad Preprints. org Google Scholar 10 and Junaid Qadir Junaid Qadir Skillet Preprints.org Google Scholar 2

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Extended version of this manuscript conference paper: Zubair, M. O’Neill, D. Al-Ali, A. Shikfa, A. Exploiting Bluetooth Vulnerabilities in e-Health IoT Devices. Proceedings of the 3rd International Conference on Future Networks and Distributed Systems, Paris, France, 1 July 2019; Page 1-7.

Received: 17 September 2022 / Revised: 11 October 2022 / Accepted: 19 October 2022 / Published: 28 October 2022

(This article belongs to the special issue of Smart Healthcare Systems based on Internet of Things and Artificial Intelligence)

Bluetooth Recurrent Application

The continued use of a wide variety of Internet of Medical Things (IoMT) devices and applications provides smart health with an ever-increasing attack surface. IoMT is a common approach to smart city solutions that can provide long-term benefits to critical infrastructure such as smart healthcare. Many IoMT devices in smart cities use Bluetooth technology for short-range communication due to its convenience, low resource consumption, and flexibility. As smart healthcare applications rely on distributed control optimization, artificial intelligence (AI) and deep learning (DL) provide effective ways to mitigate cyber-attacks. This paper presents a decentralized, predictive, DL-based process to autonomously detect and block malicious traffic and provide end-to-end protection against network attacks on IoMT devices. Additionally, we provide a BlueTalk dataset for Bluetooth-based attacks against IoMT networks. To our knowledge, this is the first intrusion detection dataset for Bluetooth Classic and Bluetooth Low Energy (BLE). Using the BlueTalk dataset, we developed a multi-layered intrusion detection method that uses deep learning techniques. We propose a decentralized architecture to implement this intrusion detection system at the edge nodes of a smart healthcare system that can be implemented in a smart city. The presented multilayer intrusion detection models achieve performance in the range of 97–99.5% based on the F1 score.

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Cities are becoming smart cities through Internet of Things (IoT) technology. Smart cities use sensing, networking and computing technologies to improve the quality of life and well-being of citizens. Such smart cities also require new service-centric computing paradigms for next-generation networks (5G, 6G and beyond) [1]. Although many network technologies are available for long-range communication, Bluetooth is the most widely used technology for short-range communication. Bluetooth is suitable for use on resource-constrained mobile devices due to its low power consumption, low cost, and support for multimedia such as data and audio streaming. Bluetooth is widely used in smart healthcare systems to enable wireless communication between smart healthcare services. Recently, Bluetooth has been prominent in its use for contact tracing applications in the fight against the global COVID-19 pandemic [2].

By 2030 [3] , the number of IoT devices is expected to grow to 124 billion. Additionally, Health Economics estimates that the market for IoT devices will grow from US$20 billion in 2015 to US$70 billion by 2025.

IoT devices in use are in the healthcare sector [4]. The widespread proliferation of IoT devices in heterogeneous networks with multiple technologies and protocols (such as Wi-Fi, Long Term Evolution (LTE), Bluetooth, and ZigBee) makes the task of securing such networks extremely complex. Research by the Information Systems Audit and Control Association (ISACA) [5] on smart cities identified the security of IoT devices as critical, many critical infrastructure elements for smart cities (such as intelligent transportation, healthcare and energy distribution) depending on their robustness, and IoT safety of smart technologies and devices [6].

As the number of Internet of Medical Things (IoMT) devices increases, the network becomes congested, causing bandwidth and latency constraints [7]. For example, an IoMT device sends data to medical professionals for routine analysis. Transmitting this data to the cloud causes delay and bandwidth congestion in the communication path [8], putting the patient’s life at risk. To address this challenge, the concept of Edge Cloud for IoMT paradigm emerged. Edge Cloud improves efficiency and provides more reliability for a smart healthcare system. Faster response time and lower energy consumption result in longer battery life of medical devices and reduce network bandwidth consumption [9, 10].

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The rapid development of IoT devices and the massive interconnection between such devices greatly opens up the potential attack surface for smart healthcare by malicious actors. IoT devices are vulnerable to various medium and high severity attacks [11]. Various vulnerabilities allow attackers to conduct a wide range of attacks such as denial of service (DoS), distributed DoS (DDoS), man-in-the-middle (MITM), data leakage and spoofing. These attacks can cause system resource availability and cause physical harm to people while the patient is in an ambulance or hospital. According to the Global Connected Industries Cyber ​​Security Report..

The target is IoT devices [11]. A potential weakness in the network, IoT device, and protocol allows attackers to access the network in a completely unauthorized manner (e.g. Mirai attack) [12]. In addition to these cyber attacks, insecure operating systems and application vulnerabilities are other major threats to healthcare. Research says.

Cyber ​​threats in the healthcare sector are related to imaging equipment, which can lead to disruptions in communication between patients and medical professionals. Furthermore,

Bluetooth Recurrent Application

Traditional security mechanisms cannot be implemented in an IoT network because the network protocol stack itself may have many vulnerabilities. Zero-day attacks are difficult to detect by conventional security mechanisms due to the computational overhead, which does not bode well with the resource-constrained nature of typical IoT devices [13]. Traditional perimeter security controls only protect against external attacks, but they fail to detect internal attacks on the network. A faster and faster detection mechanism is needed to guarantee IoT network security to counter new threats before the network is compromised.

Bluetooth Sensors: The Definitive Guide Learn Basics

In this article, we focus on Bluetooth communication security in smart health systems. After reviewing the key security issues, we focus on detecting wireless attacks against IoMT. Wireless attacks are carried out when data is at rest on different channels or transferred from one device to another over a wireless medium using different protocols such as Bluetooth Low Energy (BLE), Bluetooth Basic Rate/Enhanced Data Rate (BR/EDR). I move. ), Wi-Fi, Long Range (LoRA), etc. The vulnerability of a wireless network is a threat to the entire network and can damage the entire system. Attackers can use various attacks to take over an IOMT device or the entire network, such as peer-to-peer, denial of service, eavesdropping, man-in-the-middle (MITM) and authentication attacks. The main contributions of this study are as follows:

The flow of this paper is organized as follows: Section 2 provides an overview of related work, followed by Section 3, which describes the proposed model, architecture, and dataset in detail. We show the performance evaluation results of the proposed model in Section 4. Finally, the future direction work concludes in Chapter 5.

IoMT devices perform various functions in smart health systems, such as recording electrical impulses through an electrocardiogram (ECG) or monitoring blood sugar or blood pressure. For ambulance-bound patients, IoMT devices monitor patient activity, store vital information about the patient’s physical symptoms, and trigger alerts.

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