IoT: Communication protocols and security threats

Gerodimos, Apostolos; Μαγλαράς, Λέανδρος; Ferrag, Mohamed Amine; Ayres, Nick; Kantzavelou, Ioanna

doi:10.1016/j.iotcps.2022.12.003

Cited by 51 publications

(22 citation statements)

References 41 publications

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Section: Related Iot Surveysmentioning

confidence: 99%

“…Another works [35,4,78,44,125] predominantly focus on communication protocols and security in IoT. Gerodimos et al [35] discuss the fundamentals of IoT architecture and present an analysis of security threats. Abbasi [4] et al and Nebbione and Calzarossa [78] delve deep into security requirements, threats, and countermeasures in the IoT application layer.…”

Section: Related Iot Surveysmentioning

confidence: 99%

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A survey on IoT application layer protocols, security challenges, and the role of explainable AI in IoT (XAIoT)

Quincozes,

Kazienko

et al. 2024

Int. J. Inf. Secur.

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The Internet of Things (IoT) plays a fundamental role in contemporary society, necessitating an in-depth comprehension of its application layer protocols, intertwined technologies, security issues, and effective countermeasures. This survey delivers an exhaustive analysis of these protocols, emphasizing the escalating significance of Explainable Artificial Intelligence in IoT (XAIoT). To elucidate its practical implications, we conducted a case study examining a real-world scenario where XAIoT significantly bolstered IoT security. This case study demonstrated XAIoT's potential to enhance transparency and trustworthiness in IoT systems. Furthermore, the survey critically evaluates existing literature, pinpointing specific opportunities and gaps in the present state of IoT application layer security. For instance, our analysis revealed a pressing need for more robust security protocols and the integration of advanced machine learning techniques for anomaly detection in IoT applications. This survey, designed to provide a comprehensive perspective, seeks to stimulate additional innovation and research in the realm of secure and intelligent IoT applications. In doing so, it contributes to the ongoing dialogue on improving IoT security, offering valuable insights for researchers and practitioners alike.

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Section: Related Iot Surveysmentioning

confidence: 99%

Section: Related Iot Surveysmentioning

confidence: 99%

A survey on IoT application layer protocols, security challenges, and the role of explainable AI in IoT (XAIoT)

Quincozes,

Kazienko

et al. 2024

Int. J. Inf. Secur.

View full text Add to dashboard Cite

show abstract

“…Robust security measures are integrated to fortify the integrity and confidentiality of processed data. This includes implementation of TLS/SSL for secure data transmission, access-control mechanisms, and intrusion detection systems [108]. Compliance with industry standards such as ISO/IEC 27001 and GDPR is ensured through continuous security audits using tools like Nessus.…”

Section: Server Setupmentioning

confidence: 99%

Real-Time Remote Patient Monitoring: A Review of Biosensors Integrated with Multi-Hop IoT Systems via Cloud Connectivity

Uddin,

Koo

2024

Applied Sciences

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This comprehensive review paper explores the intricate integration of biosensors with multi-hop Internet of Things (IoT) systems, representing a paradigm shift in healthcare through real-time remote patient monitoring. The strategic deployment of biosensors in different locations in medical facilities, intricately connected to multiple microcontrollers, serves as a cornerstone in the establishment of robust multi-hop IoT networks. This paper highlights the role of this multi-hop IoT network, which efficiently facilitates the seamless transmission of vital health data to a centralized server. Crucially, the utilization of cloud connectivity emerges as a linchpin in this integration, providing a secure and scalable platform for remote patient monitoring. This cloud-based approach not only improves the accessibility of critical health information but also transcends physical limitations, allowing healthcare providers to monitor patients in real-time from any location. This paper highlights the transformative potential of this integration in overcoming traditional healthcare limitations through real-time remote patient monitoring.

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“…In this paper, the secured communication protocols have been studied to improve the detection rate of the proposed IDS. The protocols analyzed have been listed in list [57]. These protocols have also been used in a wide range of applications as well.…”

Section: ) Communication Protocolsmentioning

confidence: 99%

“…A probabilistic model governs each intrusion at the IoT node. The proposed IDS needs to align with the probabilistic model of successful attacks explained in section III-D3b [57]. While quantum vulnerabilities are beyond the scope of this paper, it's important to note that the advent of quantum computing presents new challenges in cybersecurity.…”

Section: A: Common Security Vulnerabilitiesmentioning

confidence: 99%

Deep-IDS: A Real-Time Intrusion Detector for IoT Nodes Using Deep Learning

Racherla,

Sripathi,

Faruqui

et al. 2024

IEEE Access

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The Internet of Things (IoT) represents a swiftly expanding sector that is pivotal in driving the innovation of today's smart services. However, the inherent resource-constrained nature of IoT nodes poses significant challenges in embedding advanced algorithms for cybersecurity, leading to an escalation in cyberattacks against these nodes. Contemporary research in Intrusion Detection Systems (IDS) predominantly focuses on enhancing IDS performance through sophisticated algorithms, often overlooking their practical applicability. This paper introduces Deep-IDS, an innovative and practically deployable Deep Learning (DL)-based IDS. It employs a Long-Short-Term-Memory (LSTM) network comprising 64 LSTM units and is trained on the CIC-IDS2017 dataset. Its streamlined architecture renders Deep-IDS an ideal candidate for edge-server deployment, acting as a guardian between IoT nodes and the Internet against Denial of Service (DoS), Distributed Denial of Service (DDoS), Brute Force (BRF), Man-in-the-Middle (MITM), and Replay (RP) Attacks. A distinctive aspect of this research is the trade-off analysis between the intrusion detection rate and the false alarm rate, facilitating the real-time performance of the Deep-IDS. The system demonstrates an exemplary detection rate of 96.8% and an overall classification accuracy of 97.67%. Furthermore, Deep-IDS achieves precision, recall, and F1-scores of 97.67%, 98.17%, and 97.91%, respectively. On average, Deep-IDS requires 1.49 seconds to identify and mitigate intrusion attempts, effectively blocking malicious traffic sources. The remarkable efficacy, swift response time, innovative design, and novel defense strategy of Deep-IDS not only secure IoT nodes but also their interconnected subnetworks, thereby positioning Deep-IDS as a leading intrusion detection system for IoT-enhanced computer networks.

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IoT: Communication protocols and security threats

Cited by 51 publications

References 41 publications

A survey on IoT application layer protocols, security challenges, and the role of explainable AI in IoT (XAIoT)

A survey on IoT application layer protocols, security challenges, and the role of explainable AI in IoT (XAIoT)

Real-Time Remote Patient Monitoring: A Review of Biosensors Integrated with Multi-Hop IoT Systems via Cloud Connectivity

Deep-IDS: A Real-Time Intrusion Detector for IoT Nodes Using Deep Learning

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