Providing secure and reliable communication for next generation networks in smart cities

Ridhawi, Ismaeel Al; Otoum, Safa; Aloqaily, Moayad; Jararweh, Yaser; Baker, Thar

doi:10.1016/j.scs.2020.102080

Cited by 103 publications

(47 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Authors in [15] have presented a solution that leverages block-chain for managing scalable IoT systems. Authors in [16] have suggested a solution that secures the communications between IoT devices and the MEC. The proposed solution adopts a learning method to identify candidates for service composition and delivery.…”

Section: Related Workmentioning

confidence: 99%

A Machine Learning Security Framework for Iot Systems

et al. 2020

View full text Add to dashboard Cite

Internet of Things security is attracting a growing attention from both academic and industry communities. Indeed, IoT devices are prone to various security attacks varying from Denial of Service (DoS) to network intrusion and data leakage. This paper presents a novel machine learning (ML) based security framework that automatically copes with the expanding security aspects related to IoT domain. This framework leverages both Software Defined Networking (SDN) and Network Function Virtualization (NFV) enablers for mitigating different threats. This AI framework combines monitoring agent and AIbased reaction agent that use ML-Models divided into network patterns analysis, along with anomalybased intrusion detection in IoT systems. The framework exploits the supervised learning, distributed data mining system and neural network for achieving its goals. Experiments results demonstrate the efficiency of the proposed scheme. In particular, the distribution of the attacks using the data mining approach is highly successful in detecting the attacks with high performance and low cost. Regarding our anomalybased intrusion detection system (IDS) for IoT, we have evaluated the experiment in a real Smart building scenario using one-class SVM. The detection accuracy of anomalies achieved 99.71%. A feasibility study is conducted to identify the current potential solutions to be adopted and to promote the research towards the open challenges.

show abstract

Section: Related Workmentioning

confidence: 99%

A Machine Learning Security Framework for Iot Systems

et al. 2020

View full text Add to dashboard Cite

show abstract

“…A mobility management architecture for the delivery of 5G-IoT Services is presented in [32]. Al Ridhawi et al [33] proposed a Mobile Edge Computing (MEC) solution that enables node collaboration among IoT devices to provide reliable and secure communication between devices and the fog layer on one hand, and the fog layer and the cloud layer on the other hand.…”

Section: Problem Statementmentioning

confidence: 99%

Scalable and Secure Big Data IoT System Based on Multifactor Authentication and Lightweight Cryptography

et al. 2020

Self Cite

View full text Add to dashboard Cite

Organizations share an evolving interest in adopting a cloud computing approach for Internet of Things (IoT) applications. Integrating IoT devices and cloud computing technology is considered as an effective approach to storing and managing the enormous amount of data generated by various devices. However, big data security of these organizations presents a challenge in the IoT-cloud architecture. To overcome security issues, we propose a cloud-enabled IoT environment supported by multifactor authentication and lightweight cryptography encryption schemes to protect big data system. The proposed hybrid cloud environment is aimed at protecting organizations' data in a highly secure manner. The hybrid cloud environment is a combination of private and public cloud. Our IoT devices are divided into sensitive and nonsensitive devices. Sensitive devices generate sensitive data, such as healthcare data; whereas nonsensitive devices generate nonsensitive data, such as home appliance data. IoT devices send their data to the cloud via a gateway device. Herein, sensitive data are split into two parts: one part of the data is encrypted using RC6, and the other part is encrypted using the Fiestel encryption scheme. Nonsensitive data are encrypted using the Advanced Encryption Standard (AES) encryption scheme. Sensitive and nonsensitive data are respectively stored in private and public cloud to ensure high security. The use of multifactor authentication to access the data stored in the cloud is also proposed. During login, data users send their registered credentials to the Trusted Authority (TA). The TA provides three levels of authentication to access the stored data: first-level authentication-read file, second-level authentication-download file, and thirdlevel authentication-download file from the hybrid cloud. We implement the proposed cloud-IoT architecture in the NS3 network simulator. We evaluated the performance of the proposed architecture using metrics such as computational time, security strength, encryption time, and decryption time.

show abstract

“…Secure and reliable communication is key to next generation sustainable and diversified networks in smart cities [21]. In the IoT, communication between connected systems can be divided into four models according to the RFC 7452 [22]: D2D (Device-to-Device), D2G (Device-to-Gateway), D2C Table 1.…”

Section: A (Application-level) Communication Protocolsmentioning

confidence: 99%

bIoTope: Building an IoT Open Innovation Ecosystem for Smart Cities

et al. 2020

View full text Add to dashboard Cite

The Internet of Things (IoT) has led towards a digital world in which everything becomes connected. Unfortunately, most of the currently marketed connected devices feed vertically-oriented closed systems (commonly referred to as vertical silos) which prevent the development of a unified global IoT. This issue is all the more valid in complex environments, such as smart cities, in which exceedingly large amounts of heterogeneous sensor data are collected, and in which platforms and stakeholders should also be able to interact and cooperate. Therefore, it is of utmost importance to move towards the creation of open IoT ecosystems to support efficient smart city service integration, discovery and composition. This paper contributes to the specifications of such an ecosystem, which has been developed as part of the EU's H2020 bIoTope project. The novelty of this ecosystem compared with the current literature is threefold: (i) it is based on the extensive use of open communication and data standards, notably O-MI and O-DF standards, that foster technical, syntactic and semantic interoperability over domains; (ii) it proposes an innovative service marketplace for data/service publication, discovery and incentivization; (iii) it integrates security functionalities at the IoT gateway level. The practicability of our ecosystem has been validated through several smart city proofs-of-concept set up in three distinct cities: Helsinki, Lyon and Brussels. Given the five major themes defined in the CITYKeys (a smart city performance indicator framework), namely People, Planet, Prosperity, Governance and Propagation, bIoTope mainly contributes to Prosperity-related metrics, as discussed in this paper.

show abstract

Providing secure and reliable communication for next generation networks in smart cities

Abstract: The version presented here may differ from the published version or from the version of the record. Please see the repository URL above for details on accessing the published version and note that access may require a subscription.

Cited by 103 publications

References 47 publications

A Machine Learning Security Framework for Iot Systems

A Machine Learning Security Framework for Iot Systems

Scalable and Secure Big Data IoT System Based on Multifactor Authentication and Lightweight Cryptography

bIoTope: Building an IoT Open Innovation Ecosystem for Smart Cities

Contact Info

Product

Resources

About