FBAD: Fog-based Attack Detection for IoT Healthcare in Smart Cities

Alrashdi, Ibrahim; Alqazzaz, Ali; Alharthi, Raed; Aloufi, Esam; Zohdy, Mohamed; Hua, Ming

doi:10.1109/uemcon47517.2019.8992963

Cited by 44 publications

(22 citation statements)

References 25 publications

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“… Alrashdi et al (2019) presented a fog-based attack detection (FBAD) architecture by utilizing an online sequential extreme learning machine (EOS-ELM) collection for monitoring of suspicious behaviors in healthcare system. They proved that the proposed architecture is effectively implemented in the decentralized fog-attack detection by comparing its efficiency to other methods.…”

Section: Results and Findingsmentioning

confidence: 99%

A systematic review of security and privacy issues in the internet of medical things; the role of machine learning approaches

Hameed

Hassan

Latiff

et al. 2021

PeerJ Computer Science

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Background The Internet of Medical Things (IoMTs) is gradually replacing the traditional healthcare system. However, little attention has been paid to their security requirements in the development of the IoMT devices and systems. One of the main reasons can be the difficulty of tuning conventional security solutions to the IoMT system. Machine Learning (ML) has been successfully employed in the attack detection and mitigation process. Advanced ML technique can also be a promising approach to address the existing and anticipated IoMT security and privacy issues. However, because of the existing challenges of IoMT system, it is imperative to know how these techniques can be effectively utilized to meet the security and privacy requirements without affecting the IoMT systems quality, services, and device’s lifespan. Methodology This article is devoted to perform a Systematic Literature Review (SLR) on the security and privacy issues of IoMT and their solutions by ML techniques. The recent research papers disseminated between 2010 and 2020 are selected from multiple databases and a standardized SLR method is conducted. A total of 153 papers were reviewed and a critical analysis was conducted on the selected papers. Furthermore, this review study attempts to highlight the limitation of the current methods and aims to find possible solutions to them. Thus, a detailed analysis was carried out on the selected papers through focusing on their methods, advantages, limitations, the utilized tools, and data. Results It was observed that ML techniques have been significantly deployed for device and network layer security. Most of the current studies improved traditional metrics while ignored performance complexity metrics in their evaluations. Their studies environments and utilized data barely represent IoMT system. Therefore, conventional ML techniques may fail if metrics such as resource complexity and power usage are not considered.

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Section: Results and Findingsmentioning

confidence: 99%

A systematic review of security and privacy issues in the internet of medical things; the role of machine learning approaches

Hameed

Hassan

Latiff

et al. 2021

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…Likewise, Carta et al [ 20 ] introduced a feature engineering technique to efficiently detect the anomalies in order to improve the performance of traditional IDSs. Similarly, Alrashdi et al [ 25 ] proposed a framework for detecting the malicious in fog-based IoT healthcare system. The authors used an ensemble of online sequential ELM to detect malicious attacks like man-in-the-middle, DDoS, etc.…”

Section: Literature Reviewmentioning

confidence: 99%

A Framework for Malicious Traffic Detection in IoT Healthcare Environment

Hussain

Abbas

Shah

et al. 2021

Sensors

139

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The Internet of things (IoT) has emerged as a topic of intense interest among the research and industrial community as it has had a revolutionary impact on human life. The rapid growth of IoT technology has revolutionized human life by inaugurating the concept of smart devices, smart healthcare, smart industry, smart city, smart grid, among others. IoT devices’ security has become a serious concern nowadays, especially for the healthcare domain, where recent attacks exposed damaging IoT security vulnerabilities. Traditional network security solutions are well established. However, due to the resource constraint property of IoT devices and the distinct behavior of IoT protocols, the existing security mechanisms cannot be deployed directly for securing the IoT devices and network from the cyber-attacks. To enhance the level of security for IoT, researchers need IoT-specific tools, methods, and datasets. To address the mentioned problem, we provide a framework for developing IoT context-aware security solutions to detect malicious traffic in IoT use cases. The proposed framework consists of a newly created, open-source IoT data generator tool named IoT-Flock. The IoT-Flock tool allows researchers to develop an IoT use-case comprised of both normal and malicious IoT devices and generate traffic. Additionally, the proposed framework provides an open-source utility for converting the captured traffic generated by IoT-Flock into an IoT dataset. Using the proposed framework in this research, we first generated an IoT healthcare dataset which comprises both normal and IoT attack traffic. Afterwards, we applied different machine learning techniques to the generated dataset to detect the cyber-attacks and protect the healthcare system from cyber-attacks. The proposed framework will help in developing the context-aware IoT security solutions, especially for a sensitive use case like IoT healthcare environment.

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“…are familiar node-based attack. Alrashdi et al [131] examined fog-based attacks like jamming, DDoS, Sybil, etc. on proposed IoT healthcare architecture that adversely inhibits fog node operation.…”

Section: Processing Layer Threatsmentioning

confidence: 99%

Security Threats and Artificial Intelligence Based Countermeasures for Internet of Things Networks: A Comprehensive Survey

et al. 2021

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The Internet of Things (IoT) has emerged as a technology capable of connecting heterogeneous nodes/objects, such as people, devices, infrastructure, and makes our daily lives simpler, safer, and fruitful. Being part of a large network of heterogeneous devices, these nodes are typically resourceconstrained and became the weakest link to the cyber attacker. Classical encryption techniques have been employed to ensure the data security of the IoT network. However, high-level encryption techniques can not be employed in IoT devices due to the limitation of resources. In addition, node security is still a challenge for network engineers. Thus, we need to explore a complete solution for IoT networks that can ensure nodes and data security. The rule-based approaches and shallow and deep machine learning algorithms-branches of Artificial Intelligence (AI)-can be employed as countermeasures along with the existing network security protocols. This paper presented a comprehensive layer-wise survey on IoT security threats, and the AIbased security models to impede security threats. Finally, open challenges and future research directions are addressed for the safeguard of the IoT network.

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FBAD: Fog-based Attack Detection for IoT Healthcare in Smart Cities

Cited by 44 publications

References 25 publications

A systematic review of security and privacy issues in the internet of medical things; the role of machine learning approaches

A systematic review of security and privacy issues in the internet of medical things; the role of machine learning approaches

A Framework for Malicious Traffic Detection in IoT Healthcare Environment

Security Threats and Artificial Intelligence Based Countermeasures for Internet of Things Networks: A Comprehensive Survey

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