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Advances in Cybersecurity for the Internet of Things

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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 November 2023) | Viewed by 13086

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Dr. Emmanouil Panaousis

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Guest Editor

Cyber Risk Lab, School of Computing and Mathematical Sciences University of Greenwich, Old Royal Naval College, London SE10 9LS, UK
Interests: IoT security; cyber forensics; security economics; artificial intelligence

Dr. Kaitai Liang

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Guest Editor

Cybersecurity Group, Delft University of Technology, Van Mourik Broekmanweg 6, 2628 XE Delft, The Netherlands
Interests: privacy-preserving machine learning; blockchain and smart contract security; post-quantum security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

There are numerous challenges to be addressed in order to take advantage of the full potential of the interconnection of all things. Naturally, these challenges include security and privacy, which can be seen as enablers of the future Internet of Things (IoT). IoT applications range from the ongoing automation of traditional manufacturing and industrial practices using modern smart technology (Industry 4.0) to smart grids, healthcare connected systems, smart farming, IoT- and AI-enabled domestic life and future 6G technology. The amount of data gathered, stored, processed and communicated by IoT devices and applications is unprecedented, while in parallel they may connect to cyber-physical systems controlling major parts of critical infrastructures. Inevitably, this creates a vast attack surface capable of inflicting intolerable cyber and physical risks.

We invite authors to contribute to this Special Issue of Sensors titled “Advances in Cybersecurity for the Internet of Things” by publishing their results of research related, but not limited, to the following topics:

- Modelling IoT security and privacy threats;

- Assessing and managing cyber-physical risks for IoT;

- Optimizing the trade-off between security and performance in IoT infrastructures;

- Defending against edge layer attacks and enabling the trustworthiness of fog nodes;

- Securing IoT applications and services that use ML/AI;

- Preserving privacy in blockchain- or machine-learning-powered IoT;

- Addressing key management in scalable IoT environments;

- Utilizing blockchain technology for IoT;

- Enhancing threat intelligence and decentralized intrusion detection for IoT;

- Analyzing economics and incentives for securing IoT;

- Securing 6G-enabled “massive” IoT;

- Supporting forensic investigations in IoT-enabled environments.

Dr. Emmanouil Panaousis
Dr. Kaitai Liang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (5 papers)

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Research

29 pages, 80697 KiB

Open AccessArticle

Modelling of the Energy Depletion Process and Battery Depletion Attacks for Battery-Powered Internet of Things (IoT) Devices

by Godlove Suila Kuaban, Erol Gelenbe, Tadeusz Czachórski, Piotr Czekalski and Julius Kewir Tangka

Sensors 2023, 23(13), 6183; https://doi.org/10.3390/s23136183 - 6 Jul 2023

Cited by 2 | Viewed by 1352

Abstract

The Internet of Things (IoT) is transforming almost every industry, including agriculture, food processing, health care, oil and gas, environmental protection, transportation and logistics, manufacturing, home automation, and safety. Cost-effective, small-sized batteries are often used to power IoT devices being deployed with limited energy capacity. The limited energy capacity of IoT devices makes them vulnerable to battery depletion attacks designed to exhaust the energy stored in the battery rapidly and eventually shut down the device. In designing and deploying IoT devices, the battery and device specifications should be chosen in such a way as to ensure a long lifetime of the device. This paper proposes diffusion approximation as a mathematical framework for modelling the energy depletion process in IoT batteries. We applied diffusion or Brownian motion processes to model the energy depletion of a battery of an IoT device. We used this model to obtain the probability density function, mean, variance, and probability of the lifetime of an IoT device. Furthermore, we studied the influence of active power consumption, sleep time, and battery capacity on the probability density function, mean, and probability of the lifetime of an IoT device. We modelled ghost energy depletion attacks and their impact on the lifetime of IoT devices. We used numerical examples to study the influence of battery depletion attacks on the distribution of the lifetime of an IoT device. We also introduced an energy threshold after which the device’s battery should be replaced in order to ensure that the battery is not completely drained before it is replaced. Full article

(This article belongs to the Special Issue Advances in Cybersecurity for the Internet of Things)

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22 pages, 2140 KiB

Open AccessArticle

Towards an Effective Intrusion Detection Model Using Focal Loss Variational Autoencoder for Internet of Things (IoT)

by Shapla Khanam, Ismail Ahmedy, Mohd Yamani Idna Idris and Mohamed Hisham Jaward

Sensors 2022, 22(15), 5822; https://doi.org/10.3390/s22155822 - 4 Aug 2022

Cited by 10 | Viewed by 2391

Abstract

As the range of security attacks increases across diverse network applications, intrusion detection systems are of central interest. Such detection systems are more crucial for the Internet of Things (IoT) due to the voluminous and sensitive data it produces. However, the real-world network produces imbalanced traffic including different and unknown attack types. Due to this imbalanced nature of network traffic, the traditional learning-based detection techniques suffer from lower overall detection performance, higher false-positive rate, and lower minority-class attack detection rates. To address the issue, we propose a novel deep generative-based model called Class-wise Focal Loss Variational AutoEncoder (CFLVAE) which overcomes the data imbalance problem by generating new samples for minority attack classes. Furthermore, we design an effective and cost-sensitive objective function called Class-wise Focal Loss (CFL) to train the traditional Variational AutoEncoder (VAE). The CFL objective function focuses on different minority class samples and scrutinizes high-level feature representation of observed data. This leads the VAE to generate more realistic, diverse, and quality intrusion data to create a well-balanced intrusion dataset. The balanced dataset results in improving the intrusion detection accuracy of learning-based classifiers. Therefore, a Deep Neural Network (DNN) classifier with a unique architecture is then trained using the balanced intrusion dataset to enhance the detection performance. Moreover, we utilize a challenging and highly imbalanced intrusion dataset called NSL-KDD to conduct an extensive experiment with the proposed model. The results demonstrate that the proposed CFLVAE with DNN (CFLVAE-DNN) model obtains promising performance in generating realistic new intrusion data samples and achieves superior intrusion detection performance. Additionally, the proposed CFLVAE-DNN model outperforms several state-of-the-art data generation and traditional intrusion detection methods. Specifically, the CFLVAE-DNN achieves 88.08% overall intrusion detection accuracy and 3.77% false positive rate. More significantly, it obtains the highest low-frequency attack detection rates for U2R (79.25%) and R2L (67.5%) against all the state-of-the-art algorithms. Full article

(This article belongs to the Special Issue Advances in Cybersecurity for the Internet of Things)

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20 pages, 1126 KiB

Open AccessArticle

Reconfigurable Security Architecture (RESA) Based on PUF for FPGA-Based IoT Devices

by Armin Babaei, Gregor Schiele and Michael Zohner

Sensors 2022, 22(15), 5577; https://doi.org/10.3390/s22155577 - 26 Jul 2022

Cited by 2 | Viewed by 2252

Abstract

Cybersecurity is a challenge in the utilization of IoT devices. One of the main security functions that we need for IoT devices is authentication. In this work, we used physical unclonable function (PUF) technology to propose a lightweight authentication protocol for IoT devices with long lifetimes. Our focus in this project is a solution for FPGA-based IoT devices. We evaluated the resiliency of our solution against state-of-the-art machine learning attacks. Full article

(This article belongs to the Special Issue Advances in Cybersecurity for the Internet of Things)

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20 pages, 2071 KiB

Open AccessArticle

A Crypto-Steganography Approach for Hiding Ransomware within HEVC Streams in Android IoT Devices

by Iman Almomani, Aala Alkhayer and Walid El-Shafai

Sensors 2022, 22(6), 2281; https://doi.org/10.3390/s22062281 - 16 Mar 2022

Cited by 14 | Viewed by 3161

Abstract

Steganography is a vital security approach that hides any secret content within ordinary data, such as multimedia. This hiding aims to achieve the confidentiality of the IoT secret data; whether it is benign or malicious (e.g., ransomware) and for defensive or offensive purposes. This paper introduces a hybrid crypto-steganography approach for ransomware hiding within high-resolution video frames. This proposed approach is based on hybridizing an AES (advanced encryption standard) algorithm and LSB (least significant bit) steganography process. Initially, AES encrypts the secret Android ransomware data, and then LSB embeds it based on random selection criteria for the cover video pixels. This research examined broad objective and subjective quality assessment metrics to evaluate the performance of the proposed hybrid approach. We used different sizes of ransomware samples and different resolutions of HEVC (high-efficiency video coding) frames to conduct simulation experiments and comparison studies. The assessment results prove the superior efficiency of the introduced hybrid crypto-steganography approach compared to other existing steganography approaches in terms of (a) achieving the integrity of the secret ransomware data, (b) ensuring higher imperceptibility of stego video frames, (3) introducing a multi-level security approach using the AES encryption in addition to the LSB steganography, (4) performing randomness embedding based on RPS (random pixel selection) for concealing secret ransomware bits, (5) succeeding in fully extracting the ransomware data at the receiver side, (6) obtaining strong subjective and objective qualities for all tested evaluation metrics, (7) embedding different sizes of secret data at the same time within the video frame, and finally (8) passing the security scanning tests of 70 antivirus engines without detecting the existence of the embedded ransomware. Full article

(This article belongs to the Special Issue Advances in Cybersecurity for the Internet of Things)

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27 pages, 2913 KiB

Open AccessArticle

Multi-Unit Serial Polynomial Multiplier to Accelerate NTRU-Based Cryptographic Schemes in IoT Embedded Systems

by Santiago Sánchez-Solano, Eros Camacho-Ruiz, Macarena C. Martínez-Rodríguez and Piedad Brox

Sensors 2022, 22(5), 2057; https://doi.org/10.3390/s22052057 - 7 Mar 2022

Cited by 5 | Viewed by 2762

Abstract

Concern for the security of embedded systems that implement IoT devices has become a crucial issue, as these devices today support an increasing number of applications and services that store and exchange information whose integrity, privacy, and authenticity must be adequately guaranteed. Modern lattice-based cryptographic schemes have proven to be a good alternative, both to face the security threats that arise as a consequence of the development of quantum computing and to allow efficient implementations of cryptographic primitives in resource-limited embedded systems, such as those used in consumer and industrial applications of the IoT. This article describes the hardware implementation of parameterized multi-unit serial polynomial multipliers to speed up time-consuming operations in NTRU-based cryptographic schemes. The flexibility in selecting the design parameters and the interconnection protocol with a general-purpose processor allow them to be applied both to the standardized variants of NTRU and to the new proposals that are being considered in the post-quantum contest currently held by the National Institute of Standards and Technology, as well as to obtain an adequate cost/performance/security-level trade-off for a target application. The designs are provided as AXI4 bus-compliant intellectual property modules that can be easily incorporated into embedded systems developed with the Vivado design tools. The work provides an extensive set of implementation and characterization results in devices of the Xilinx Zynq-7000 and Zynq UltraScale+ families for the different sets of parameters defined in the NTRUEncrypt standard. It also includes details of their plug and play inclusion as hardware accelerators in the C implementation of this public-key encryption scheme codified in the LibNTRU library, showing that acceleration factors of up to 3.1 are achieved when compared to pure software implementations running on the processing systems included in the programmable devices. Full article

(This article belongs to the Special Issue Advances in Cybersecurity for the Internet of Things)

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