IoT Security

Jalbani, Khuda Bux; Jalbani, Akhtar Hussain; Soomro, Saima Siraj

doi:10.4018/978-1-7998-2803-7.ch006

Cited by 5 publications

(3 citation statements)

References 21 publications

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“…This is implemented for the security of a patient's privacy and data collected from these IoT devices. By following, these authors [70] recommended a method against device forging at the physical layer and security of data at transit or rest. Due to this system, it will be easy for the health department of any country to locate these patients or any other patients with different diseases.…”

Section: Resultsmentioning

confidence: 99%

IoT-Based Healthcare Support System for Alzheimer’s Patients

Oskouei

MousaviLou

Bakhtiari

et al. 2020

Wireless Communications and Mobile Computing

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In the last decade, the Internet of Things (IoT) has become a new technology that aims to facilitate life and help people in all aspects of their lives. This technology is used for smart homes, smart grid stations, smart agriculture, health systems, transport services, smart cities, etc. The number of sensors and IoT devices along with applications is used for monitoring the health condition of patients. These devices will monitor the movement of targeted patients at home or out of their homes. Based on their behavior and movement, the treatment will be provided to Alzheimer’s patients. The data will be collected from multiple sensors installed at patient’s homes and smartwatches for checking their blood pressure level and temperature, which is too important in the current Corona Virus Disease (COVID-19) pandemic for these types of patients. On the other hand, due to the diminishing mobility of people around the world, increasing environmental pollution and stress which is caused by modern machine life and various brain and neurological diseases including Alzheimer’s, Parkinson, etc. are widespread among people all over the world. The different types of communication protocols such as Message Queue Telemetry Transport (MQTT) and WebSocket (with authentication and autoclosing of connection) for sensors and the smartwatch have been used. The secure backend admin panel is used for tracing the location of doctors, patients, and ambulance. These protocols are implemented with security to protect the privacy of patients also.

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Section: Resultsmentioning

confidence: 99%

IoT-Based Healthcare Support System for Alzheimer’s Patients

Oskouei

MousaviLou

Bakhtiari

et al. 2020

Wireless Communications and Mobile Computing

Self Cite

View full text Add to dashboard Cite

show abstract

“…The obtained accuracy result is illustrated in Figure 4. The introduced ESCNN approach compared with existing research approaches such as Particle swarm optimization with gradient descent algorithm (PSO-Light) [30], genetic optimized deep belief network (GA-DBN) algorithm [33], Two-tier classification model, and dimension reduction algorithm (TT-DR) [34]. The above Table 3 clearly, indicates that the ESCNN approach recognizes the attacks with a maximum detection rate (98.9%).…”

Section: Accuracymentioning

confidence: 91%

“…Protocol Verification [33] The suspicious activities are predicted by checking the protocol field. However, the false-positive rate is produced due to the unspecified protocols.…”

mentioning

confidence: 99%

Threat Analysis in IOT Network Using Evolutionary Sparse Convolute Network Intrusion Detection System

Raheema¹

2023

Int. J. Onl. Eng.

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Internet of Things (IoT) played a crucial role in various sectors such as automobiles and the logistic tracking medical field because it consists of distributed nodes, servers, and software for effective communication. Although, this IoT paradigm suffered from intrusion threats and attacks that cause security and privacy issues. Existing intrusion detection techniques fail to maintain reliability against the attacks. Therefore, in this work, IoT intrusion threat has been analyzed by using the sparse convolute network to contest the threats and attacks. The network is trained using sets of intrusion data, characteristics, and suspicious activities, which helps identify and track the attacks, mainly Distributed Denial of Service (DDoS) attacks. Along with this, the network is optimized using evolutionary techniques that identify and detect the regular, error, and intrusion attempts under different conditions. The sparse network forms the complex hypotheses evaluated using neurons, and the obtained event stream outputs are propagated to further hidden layer processes. This process minimizes the intrusion involvement in IoT data transmission. The effective utilization of training patterns in the network classifies the standard and threat patterns successfully. Then the effectiveness of the system is evaluated using experimental results and discussion.

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Cyber-physical security for IoT networks: a comprehensive review on traditional, blockchain and artificial intelligence based key-security

Attkan

Ranga

2022

Complex Intell. Syst.

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The recent years have garnered huge attention towards the Internet of Things (IoT) because it enables its consumers to improve their lifestyles and professionally keep up with the technological advancements in the cyber-physical world. The IoT edge devices are heterogeneous in terms of the technology they are built on and the storage file formats used. These devices require highly secure modes of mutual authentication to authenticate each other before actually sending the data. Mutual authentication is a very important aspect of peer-to-peer communication. Secure session keys enable these resource-constrained devices to authenticate each other. After successful authentication, a device can be authorized and can be granted access to shared resources. The need for validating a device requesting data transfer to avoid data privacy breaches that may compromise confidentiality and integrity. Blockchain and artificial intelligence (AI) both are extensively being used as an integrated part of IoT networks for security enhancements. Blockchain provides a decentralized mechanism to store validated session keys that can be allotted to the network devices. Blockchain is also used to load balance the stressing edge devices during low battery levels. AI on the other hand provides better learning and adaptiveness towards IoT attacks. The integration of newer technologies in IoT key management yields enhanced security features. In this article, we systematically survey recent trending technologies from an IoT security point of view and discuss traditional key security mechanisms. This article delivers a comprehensive quality study for researchers on authentication and session keys, integrating IoT with blockchain and AI-based authentication in cybersecurity.

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IoT Security

Cited by 5 publications

References 21 publications

IoT-Based Healthcare Support System for Alzheimer’s Patients

IoT-Based Healthcare Support System for Alzheimer’s Patients

Threat Analysis in IOT Network Using Evolutionary Sparse Convolute Network Intrusion Detection System

Cyber-physical security for IoT networks: a comprehensive review on traditional, blockchain and artificial intelligence based key-security

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