LiKe: Lightweight Certificateless Key Agreement for Secure IoT Communications

Tedeschi, Pietro; Sciancalepore, Savio; Eliyan, Areej; Pietro, Roberto Di

doi:10.1109/jiot.2019.2953549

Cited by 58 publications

(31 citation statements)

References 34 publications

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“…Okada et al used indirect transmission in Zigbee and launched a low-rate denial of service attack that eluded the preventive measures [41]. In [42], the author proposed a certificate-less key agreement protocol that uses elliptic curve cryptography and prevents impersonating attacks.…”

Section: Zigbee Technologymentioning

confidence: 99%

“…Therefore, we have analyzed the existing literature related to Zigbee and identified that the protocol uses weak authentication in terms of installation code during the association phase [38], which makes the protocol vulnerable to a variety of attacks [39]. Researchers have proposed different approaches to ensure prevention against these attacks [35] [40] [42], but the proposed solution opens the door for new variants of cyber-attacks [41]. Therefore, we have proposed the ZigbeeSHF protocol, which considers the limitations of existing studies and provides prevention against different attacks, such as device control, eavesdropping, fake device injection, malicious insider, man-in-the-middle, masquerading, message tampering, privacy leakage, and replay.…”

Section: Analysis Of Literature Surveymentioning

confidence: 99%

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Secure Health Fog: A Novel Framework for Personalized Recommendations Based on Adaptive Model Tuning

2021

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The emergence of smart technology has equipped humans with wearables and sensors that collect relevant data related to individuals and their surroundings. In healthcare, the collected data can monitor user emotion, behavior, and activity, which leads to the development of personalized decisions and improves lifestyle. In this paper, we analyzed the existing health fog framework and identified its limitations in terms of security, performance, and accuracy. Based on these limitations, we propose a secure health fog (SHF) framework that collects data from different Internet of Things (IoT) devices and maintains a personalized repository for adaptive model tuning. The adaptive model improves periodically based on user feedback and generates a personalized recommendation. Moreover, the existing IoT devices mostly rely on low-cost and low-power Zigbee technology, which is vulnerable to different attacks, such as device control, eavesdropping, fake device injection, malicious insider, man-in-the-middle, masquerading, message tampering, privacy leakage, and replay attack. Therefore, we propose a Zigbee Secure Health Fog (ZigbeeSHF) protocol, which uses symmetric and public-key cryptography to prevent these attacks. For data migration security, we concatenate the encrypted data with the encrypted digital signature to provide data authenticity, integrity, and confidentiality. To support our claims, we use the automated formal verification tools Scyther and AVISPA (Automated Validation of Internet Security Protocols and Applications), which evaluate the protocols based on the threat model and exploits the vulnerabilities in different attacking environments. The results of both tools ensure prevention against the mentioned attacks. As a proof of concept, we also evaluate the accuracy and performance of our proposed framework in a smart studio apartment. The result shows that the adaptive model tuned for an individual user is very effective, and the average accuracy of 32.5% is improved after one month. Furthermore, the proposed ZigbeeSHF protocol requires 7.93% and 25.35% more computation time than Zigbee with and without installation code, respectively.INDEX TERMS Secure health fog, adaptive model tuning, personalized decision, security, Zigbee I. INTRODUCTIONWith the emergence of smart technology and ubiquitous computing, different sensors and wearable devices can collect information related to a specific user and their surrounding environment. The information collected from these sensors can facilitate the user in their daily life, such as task reminder, medication intake, fitness, adjustment of room temperature, lighting, ventilation, etc. Such innovation makes the user's

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Section: Zigbee Technologymentioning

confidence: 99%

Section: Analysis Of Literature Surveymentioning

confidence: 99%

Secure Health Fog: A Novel Framework for Personalized Recommendations Based on Adaptive Model Tuning

2021

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“…The proposed framework can effectively defend against different types of prevalent exploits in IoT settings, including man-in-the-middle (MITM), eavesdropping, packet manipulation, replay, and known-key [ 37 ]. The proposed framework is secured against the MITM exploit by the mutual authentication provided by TLS for any communications between a station/AP and the

[ 38 ].…”

Section: Secure Offloading Frameworkmentioning

confidence: 99%

“…The proposed framework is secured against the MITM exploit by the mutual authentication provided by TLS for any communications between a station/AP and the

[ 38 ]. For eavesdropping attacks, even if an eavesdropper manages to capture any packets, the encryption mechanism ensures that the eavesdropper cannot extract any meaningful data from the sniffed packet [ 37 ]. This is because each packet is either part of the end-to-end TLS session or protected by a previously distributed pre-shared key between a station and its associated AP.…”

Section: Secure Offloading Frameworkmentioning

confidence: 99%

A Framework for Managing Device Association and Offloading the Transport Layer’s Security Overhead of WiFi Device to Access Points

Nofal

Tran

Dezfouli

et al. 2021

Sensors

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Considering the resource constraints of Internet of Things (IoT) stations, establishing secure communication between stations and remote servers imposes a significant overhead on these stations in terms of energy cost and processing load. This overhead, in particular, is considerable in networks providing high communication rates and frequent data exchange, such as those relying on the IEEE 802.11 (WiFi) standard. This paper proposes a framework for offloading the processing overhead of secure communication protocols to WiFi access points (APs) in deployments where multiple APs exist. Within this framework, the main problem is finding the AP with sufficient computation and communication capacities to ensure secure and efficient transmissions for the stations associated with that AP. Based on the data-driven profiles obtained from empirical measurements, the proposed framework offloads most heavy security computations from the stations to the APs. We model the association problem as an optimization process with a multi-objective function. The goal is to achieve maximum network throughput via the minimum number of APs while satisfying the security requirements and the APs’ computation and communication capacities. The optimization problem is solved using genetic algorithms (GAs) with constraints extracted from a physical testbed. Experimental results demonstrate the practicality and feasibility of our comprehensive framework in terms of task and energy efficiency as well as security.

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“…According to Statista’s [ 8 ] research institutions, there will be 31 billion connected devices by 2020 and 75 billion devices by 2025, which shows that the overall ecological development is rapid. While many IoT service platforms [ 9 , 10 , 11 ] have been developed to integrate these devices in recent years, most of them are nonstandard service and management architectures, which have potential security issues.…”

Section: Introductionmentioning

confidence: 99%

A Framework for Off-Line Operation of Smart and Traditional Devices of IoT Services

Huang

2020

Sensors

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Recently, with the continuous evolution of information technology, various products such as Building Information, Internet of Things (IoT), Big Data, Cloud Computing and Machine Learning have been developed and have created a lifestyle change. A smart Internet of Things (IoT) system is formed by combining the communication capabilities of the internet with control, monitoring and identification services to integrate people, things and objects. However, in some IoT environments that have a weak signal, such as remote areas, warehouses or basements, the network may become unstable, meaning that the IoT system is unable to provide efficient services. This paper therefore presents a framework that ensures the reliability of IoT system services so that even if the IoT system cannot connect to the network, the system can provide the services offline. To avoid increasing the installation cost or replacing existing traditional devices with modern smart devices, this framework can also be used to control traditional devices. The system operation is convenient because users can operate all their smart and traditional devices under the IoT system through voice commands and/or a handheld microcontroller, thus reducing the manual operation of the user. The framework proposed in this paper can be applied to various smart scenarios, including smart warehouses, smart restaurants, smart homes, smart farms and smart factories, to improve people’s quality of life and convenience, and create a humane and comfortable smart living environment.

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LiKe: Lightweight Certificateless Key Agreement for Secure IoT Communications

Cited by 58 publications

References 34 publications

Secure Health Fog: A Novel Framework for Personalized Recommendations Based on Adaptive Model Tuning

Secure Health Fog: A Novel Framework for Personalized Recommendations Based on Adaptive Model Tuning

A Framework for Managing Device Association and Offloading the Transport Layer’s Security Overhead of WiFi Device to Access Points

A Framework for Off-Line Operation of Smart and Traditional Devices of IoT Services

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