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Cyber Security in IoT Era

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 21483

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Special Issue Editors

Prof. Dr. Leandros Maglaras

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Guest Editor

School of Computer Science and Informatics, De Montfort University, Leicester LE1 9BH, UK
Interests: cyber security; privacy preservation; risk management; routing protocols; vehicular networks; social network analysis; power electronics
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Helge Janicke

E-Mail Website
Guest Editor

Cyber Security Cooperative Research Centre, Building 15 Level 2/270 Joondalup Dr, Joondalup, WA 6027, Australia
Interests: cyber security of critical infrastructure; operational technology; IIoT and IoT; access control; policy-based management; cyber-warfare and peacekeeping
Special Issues, Collections and Topics in MDPI journals

Dr. Sharif Abuadbba

E-Mail Website
Guest Editor

Cybersecurity Research Scientist - Data61 CSIRO
Interests: AI for cybersecurity; privacy and security; IoT; cryptography and watermarking

Special Issue Information

Dear Colleagues,

The rapid development and fast paced adoption of smart Internet of Things (IoT) devices throughout our homes, cities, and in the case of the Industrial Internet of Things (IIoT) our transportation systems and industries offers enormous opportunities for our economies through improving resources and service utilisation that are in many cases transforming the way we live and work.

IoT/IIoT devices are a key to maintain and improve health and social care in countries that face increasing pressure on their age-care systems due to increasing life-spans and shifts in demography. Similarly they track fitness, minimise our energy usage at home. In our factories of the future IIoT devices will form a key part of digitised production processes.

In this call we are welcoming blue-sky and applied research covering novel and transformative ideas and applications in both IoT in a consumer setting as well as IIoT as a successor for current operational technologies.

Dr. Leandros Maglaras
Prof. Dr. Helge Janicke
Dr. Sharif Abuadbba
Guest Editors

Manuscript Submission Information

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Keywords

IoT/IIoT secure network infrastructure
IoT/IIoT security protocols and communication security
Security by design for IoT/IIoT
Secure IoT/IIoT Through Life Lifecycle Management and Certification Schemes
IoT/IIoT security analysis and audit
Secure Sensing and Secure IoT/IIoT Data processing
Privacy in IoT/IIoT
Novel security architectures, and security applications for IoT/IIoT
Federated and interpretable Learning for IoT/IIoT Security
Security testbeds and experimental results for IoT/IIoT
Trust management in IoT/IIoT
Access control for IoT/IIoT (Authentication, Authorisation, Audit, Information Flow Controls)
Threat modelling and Risk assessment in IoT/IIoT
Intrusion detection for IoT/IIoT
Digital forensics in IoT/IIoT
Ethics and legal considerations in IoT/IIoT

Published Papers (6 papers)

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Research

18 pages, 2712 KiB

Open AccessArticle

A Hybrid Deep Learning-Driven SDN Enabled Mechanism for Secure Communication in Internet of Things (IoT)

by Danish Javeed, Tianhan Gao, Muhammad Taimoor Khan and Ijaz Ahmad

Sensors 2021, 21(14), 4884; https://doi.org/10.3390/s21144884 - 18 Jul 2021

Cited by 43 | Viewed by 4775

Abstract

The Internet of Things (IoT) has emerged as a new technological world connecting billions of devices. Despite providing several benefits, the heterogeneous nature and the extensive connectivity of the devices make it a target of different cyberattacks that result in data breach and financial loss. There is a severe need to secure the IoT environment from such attacks. In this paper, an SDN-enabled deep-learning-driven framework is proposed for threats detection in an IoT environment. The state-of-the-art Cuda-deep neural network, gated recurrent unit (Cu- DNNGRU), and Cuda-bidirectional long short-term memory (Cu-BLSTM) classifiers are adopted for effective threat detection. We have performed 10 folds cross-validation to show the unbiasedness of results. The up-to-date publicly available CICIDS2018 data set is introduced to train our hybrid model. The achieved accuracy of the proposed scheme is 99.87%, with a recall of 99.96%. Furthermore, we compare the proposed hybrid model with Cuda-Gated Recurrent Unit, Long short term memory (Cu-GRULSTM) and Cuda-Deep Neural Network, Long short term memory (Cu- DNNLSTM), as well as with existing benchmark classifiers. Our proposed mechanism achieves impressive results in terms of accuracy, F1-score, precision, speed efficiency, and other evaluation metrics. Full article

(This article belongs to the Special Issue Cyber Security in IoT Era)

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17 pages, 15805 KiB

Open AccessArticle

Managing Trust and Detecting Malicious Groups in Peer-to-Peer IoT Networks

by Alanoud Alhussain, Heba Kurdi and Lina Altoaimy

Sensors 2021, 21(13), 4484; https://doi.org/10.3390/s21134484 - 30 Jun 2021

Cited by 7 | Viewed by 2060

Abstract

Peer-to-peer (P2P) networking is becoming prevalent in Internet of Thing (IoT) platforms due to its low-cost low-latency advantages over cloud-based solutions. However, P2P networking suffers from several critical security flaws that expose devices to remote attacks, eavesdropping and credential theft due to malicious peers who actively work to compromise networks. Therefore, trust and reputation management systems are emerging to address this problem. However, most systems struggle to identify new smart models of malicious peers, especially those who cooperate together to harm other peers. This paper proposes an intelligent trust management system, namely, Trutect, to tackle this issue. Trutect exploits the power of neural networks to provide recommendations on the trustworthiness of each peer. The system identifies the specific model of an individual peer, whether good or malicious. The system also detects malicious collectives and their suspicious group members. The experimental results show that compared to rival trust management systems, Trutect raises the success rates of good peers at a significantly lower running time. It is also capable of accurately identifying the peer model. Full article

(This article belongs to the Special Issue Cyber Security in IoT Era)

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26 pages, 5498 KiB

Open AccessArticle

Performance Analysis of the IEEE 802.15.4 Protocol for Smart Environments under Jamming Attacks

by Nicolás López-Vilos, Claudio Valencia-Cordero, Cesar Azurdia-Meza, Samuel Montejo-Sánchez and Samuel Baraldi Mafra

Sensors 2021, 21(12), 4079; https://doi.org/10.3390/s21124079 - 13 Jun 2021

Cited by 3 | Viewed by 3656

Abstract

Jamming attacks in wireless sensor networks (WSNs) scenarios are detrimental to the performance of these networks and affect the security and stability of the service perceived by users. Therefore, the evaluation of the effectiveness of smart environment platforms based on WSNs has to consider the system performance when data collection is executed under jamming attacks. In this work, we propose an experimental testbed to analyze the performance of a WSN using the IEEE 802.15.4 CSMA/CA unslotted mode under jamming attacks in terms of goodput, packet receive rate (PRR), and energy consumption to assess the risk for users and the network in the smart scenario. The experimental results show that constant and reactive jamming strategies severely impact the evaluated performance metrics and the variance’ of the received signal strength (RSS) for some signal-to-interference-plus-noise ratio (SINR) ranges. The measurements obtained using the experimental testbed were correlated with analytical models. The results show that in the presence of one interferer, for SINR values higher than

4.5

dB, the PRR is almost

0.99

, and the goodput

3.05

Kbps, but the system performance is significantly degraded when the amount of interferers increases. Additionally, the energy efficiency associated with reactive strategies is superior to the constant attack strategy. Finally, based on the evaluated metrics and with the proposed experimental testbed, our findings offer a better understanding of jamming attacks on the sensor devices in real smart scenarios. Full article

(This article belongs to the Special Issue Cyber Security in IoT Era)

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13 pages, 712 KiB

Open AccessArticle

Physical Unclonable Function Based on the Internal State Transitions of a Fibonacci Ring Oscillator

by Łukasz Matuszewski, Jakub Nikonowicz, Paweł Kubczak and Wiktor Woźniak

Sensors 2021, 21(11), 3920; https://doi.org/10.3390/s21113920 - 07 Jun 2021

Cited by 4 | Viewed by 2264

Abstract

This article introduces a new class of physical unclonable functions (PUFs) based on the Fibonacci ring oscillator (FIRO). The research conducted here proves that before reaching the desired randomness, the oscillator shows a certain degree of repeatability and uniqueness in the initial sequence of internal state transitions. The use of an FIRO in conjunction with the restart method makes it possible to obtain a set of short boot sequences, which are processed with an innovative feature extraction algorithm that enables reliable device identification. This approach ensures the reuse of the existing random number generator (RNG), rather than multiplying ring oscillators in a dedicated structure. Moreover, the algorithm for the recovery of the device key from the boot set can be successfully implemented in the authorizing center, thus significantly releasing the resources of authorized low-complexity devices. The proposed methodology provides an easily obtainable key with identifiability, which was proven experimentally on FPGAs from different manufacturers. Full article

(This article belongs to the Special Issue Cyber Security in IoT Era)

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21 pages, 1061 KiB

Open AccessArticle

WHISPER: A Location Privacy-Preserving Scheme Using Transmission Range Changing for Internet of Vehicles

by Messaoud Babaghayou, Nabila Labraoui, Ado Adamou Abba Ari, Mohamed Amine Ferrag, Leandros Maglaras and Helge Janicke

Sensors 2021, 21(7), 2443; https://doi.org/10.3390/s21072443 - 01 Apr 2021

Cited by 13 | Viewed by 3554

Abstract

Internet of Vehicles (IoV) has the potential to enhance road-safety with environment sensing features provided by embedded devices and sensors. This benignant feature also raises privacy issues as vehicles announce their fine-grained whereabouts mainly for safety requirements, adversaries can leverage this to track and identify users. Various privacy-preserving schemes have been designed and evaluated, for example, mix-zone, encryption, group forming, and silent-period-based techniques. However, they all suffer inherent limitations. In this paper, we review these limitations and propose WHISPER, a safety-aware location privacy-preserving scheme that adjusts the transmission range of vehicles in order to prevent continuous location monitoring. We detail the set of protocols used by WHISPER, then we compare it against other privacy-preserving schemes. The results show that WHISPER outperformed the other schemes by providing better location privacy levels while still fulfilling road-safety requirements. Full article

(This article belongs to the Special Issue Cyber Security in IoT Era)

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27 pages, 1874 KiB

Open AccessArticle

A Blockchain-Based Multi-Mobile Code-Driven Trust Mechanism for Detecting Internal Attacks in Internet of Things

by Noshina Tariq, Muhammad Asim, Farrukh Aslam Khan, Thar Baker, Umair Khalid and Abdelouahid Derhab

Sensors 2021, 21(1), 23; https://doi.org/10.3390/s21010023 - 22 Dec 2020

Cited by 34 | Viewed by 3914

Abstract

A multitude of smart things and wirelessly connected Sensor Nodes (SNs) have pervasively facilitated the use of smart applications in every domain of life. Along with the bounties of smart things and applications, there are hazards of external and internal attacks. Unfortunately, mitigating internal attacks is quite challenging, where network lifespan (w.r.t. energy consumption at node level), latency, and scalability are the three main factors that influence the efficacy of security measures. Furthermore, most of the security measures provide centralized solutions, ignoring the decentralized nature of SN-powered Internet of Things (IoT) deployments. This paper presents an energy-efficient decentralized trust mechanism using a blockchain-based multi-mobile code-driven solution for detecting internal attacks in sensor node-powered IoT. The results validate the better performance of the proposed solution over existing solutions with 43.94% and 2.67% less message overhead in blackhole and greyhole attack scenarios, respectively. Similarly, the malicious node detection time is reduced by 20.35% and 11.35% in both blackhole and greyhole attacks. Both of these factors play a vital role in improving network lifetime. Full article

(This article belongs to the Special Issue Cyber Security in IoT Era)

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