Security for Connected Embedded Devices

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Cybersecurity".

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 78931

Special Issue Editor

School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK
Interests: embedded systems; information security and privacy; MPSoC; DVFS; computer systems engineering; heterogeneous architecture; artificial intelligence; machine learning; signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smartphones to smart wearables to smart devices in our smart homes have become an integral part of our daily lives. We heavily rely on these low power wireless cyber-physical systems to manage a plethora of tasks such as controlling heating or lights at home, watching our favourite videos, playing mobile games, ordering food, and managing our social and active lives. Now, the advent of 5G technology in the commercial level is only going to boost connectivity among these embedded devices, which means we will be more connected than ever. Apart from having constraints on computation, power consumption, memory and thermal behaviour on these embedded devices, security is one of the key challenges that need to be addressed in these devices. These embedded devices have to face many different hostile security threats such as physical, logical/software-based and side-channel/lateral attacks.

This Special Issue aims to bring together researchers and practitioners from academia and industry to discuss aspects of security methodologies for connected embedded devices, explore new theories, investigate already deployed algorithms, protocols and schemes and innovate new solutions for overcoming the huge challenges in this important research area.

Topics include but are not limited to:

  • Embedded system security
  • Embedded machine learning based security
  • Wireless security
  • 5G security for embedded devices
  • Analysis of network and security protocols
  • Attacks with novel insights, techniques or results
  • Security of hardware designs and implementation
  • Security analysis of program source code and binaries
  • Security analysis of mobile applications
  • Side channels
  • Covert channels
  • Analysis of deployed cryptography and cryptographic protocols
  • Methods for detection of malicious or counterfeit programs and applications
  • Methods for detection of malicious or counterfeit hardware
  • New cryptographic protocols with real-world applications

Dr. Somdip Dey
Guest Editor

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. Future Internet is an international peer-reviewed open access monthly 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 1600 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.

Keywords

  • wireless security
  • embedded systems
  • mobile systems
  • low power devices
  • side channels
  • covert channels
  • security analysis

Published Papers (4 papers)

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Research

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17 pages, 781 KiB  
Article
Channel Characterization and SC-FDM Modulation for PLC in High-Voltage Power Lines
by Jose Alberto Del Puerto-Flores, José Luis Naredo, Fernando Peña-Campos, Carolina Del-Valle-Soto, Leonardo J. Valdivia and Ramón Parra-Michel
Future Internet 2022, 14(5), 139; https://doi.org/10.3390/fi14050139 - 30 Apr 2022
Cited by 2 | Viewed by 2017
Abstract
Digital communication over power lines is an active field of research and most studies in this field focus on low-voltage (LV) and medium-voltage (MV) power systems. Nevertheless, as power companies are starting to provide communication services and as smart-grid technologies are being incorporated [...] Read more.
Digital communication over power lines is an active field of research and most studies in this field focus on low-voltage (LV) and medium-voltage (MV) power systems. Nevertheless, as power companies are starting to provide communication services and as smart-grid technologies are being incorporated into power networks, high-voltage (HV) power-line communication has become attractive. The main constraint of conventional HV power-line carrier (PLC) systems is their unfeasibility for being migrated to wideband channels, even with a high signal-to-noise ratio (SNR). In this scenario, none of the current linear/non-linear equalizers used in single carrier schemes achieve the complete compensation of the highly dispersive conditions, which limits their operation to 4 kHz channels. In this paper, a new PLC-channel model is introduced for transmission lines incorporating the effects of the coupling equipment. In addition, the use of the single-carrier frequency-division modulation (SC-FDM) is proposed as a solution to operate PLC systems in a wide bandwidth, achieving transmission speeds above those of the conventional PLC system. The results presented in this paper demonstrate the superior performance of the SC-FDM-PLC over conventional PLC systems, obtaining a higher transmission capacity in 10 to 30 times. Full article
(This article belongs to the Special Issue Security for Connected Embedded Devices)
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18 pages, 1654 KiB  
Article
Improving the Robustness of Model Compression by On-Manifold Adversarial Training
by Junhyung Kwon and Sangkyun Lee
Future Internet 2021, 13(12), 300; https://doi.org/10.3390/fi13120300 - 25 Nov 2021
Cited by 1 | Viewed by 2628
Abstract
Despite the advance in deep learning technology, assuring the robustness of deep neural networks (DNNs) is challenging and necessary in safety-critical environments, including automobiles, IoT devices in smart factories, and medical devices, to name a few. Furthermore, recent developments allow us to compress [...] Read more.
Despite the advance in deep learning technology, assuring the robustness of deep neural networks (DNNs) is challenging and necessary in safety-critical environments, including automobiles, IoT devices in smart factories, and medical devices, to name a few. Furthermore, recent developments allow us to compress DNNs to reduce the size and computational requirements of DNNs to fit them into small embedded devices. However, how robust a compressed DNN can be has not been well studied in addressing its relationship to other critical factors, such as prediction performance and model sizes. In particular, existing studies on robust model compression have been focused on the robustness against off-manifold adversarial perturbation, which does not explain how a DNN will behave against perturbations that follow the same probability distribution as the training data. This aspect is relevant for on-device AI models, which are more likely to experience perturbations due to noise from the regular data observation environment compared with off-manifold perturbations provided by an external attacker. Therefore, this paper investigates the robustness of compressed deep neural networks, focusing on the relationship between the model sizes and the prediction performance on noisy perturbations. Our experiment shows that on-manifold adversarial training can be effective in building robust classifiers, especially when the model compression rate is high. Full article
(This article belongs to the Special Issue Security for Connected Embedded Devices)
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10 pages, 869 KiB  
Article
ThermalAttackNet: Are CNNs Making It Easy to Perform Temperature Side-Channel Attack in Mobile Edge Devices?
by Somdip Dey, Amit Kumar Singh and Klaus McDonald-Maier
Future Internet 2021, 13(6), 146; https://doi.org/10.3390/fi13060146 - 31 May 2021
Cited by 5 | Viewed by 71066
Abstract
Side-channel attacks remain a challenge to information flow control and security in mobile edge devices till this date. One such important security flaw could be exploited through temperature side-channel attacks, where heat dissipation and propagation from the processing cores are observed over time [...] Read more.
Side-channel attacks remain a challenge to information flow control and security in mobile edge devices till this date. One such important security flaw could be exploited through temperature side-channel attacks, where heat dissipation and propagation from the processing cores are observed over time in order to deduce security flaws. In this paper, we study how computer vision-based convolutional neural networks (CNNs) could be used to exploit temperature (thermal) side-channel attack on different Linux governors in mobile edge device utilizing multi-processor system-on-chip (MPSoC). We also designed a power- and memory-efficient CNN model that is capable of performing thermal side-channel attack on the MPSoC and can be used by industry practitioners and academics as a benchmark to design methodologies to secure against such an attack in MPSoC. Full article
(This article belongs to the Special Issue Security for Connected Embedded Devices)
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Review

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32 pages, 807 KiB  
Review
Securing IoT Devices against Differential-Linear (DL) Attack Used on Serpent Algorithm
by Khumbelo Muthavhine and Mbuyu Sumbwanyambe
Future Internet 2022, 14(2), 55; https://doi.org/10.3390/fi14020055 - 13 Feb 2022
Cited by 1 | Viewed by 2356
Abstract
Cryptographic algorithms installed on Internet of Things (IoT) devices suffer many attacks. Some of these attacks include the differential linear attack (DL). The DL attack depends on the computation of the probability of differential-linear characteristics, which yields a Differential-Linear Connectivity Table (DLCT [...] Read more.
Cryptographic algorithms installed on Internet of Things (IoT) devices suffer many attacks. Some of these attacks include the differential linear attack (DL). The DL attack depends on the computation of the probability of differential-linear characteristics, which yields a Differential-Linear Connectivity Table (DLCT). The DLCT is a probability table that provides an attacker many possibilities of guessing the cryptographic keys of any algorithm such as Serpent. In essence, the attacker firstly constructs a DLCT by using building blocks such as Substitution Boxes (S-Boxes) found in many algorithms’ architectures. In depth, this study focuses on securing IoT devices against DL attacks used on Serpent algorithms by using three magic numbers mapped on a newly developed mathematical function called Blocker, which will be added on Serpent’s infrastructure before being installed in IoT devices. The new S-Boxes with 32-bit output were generated to replace the original Serpent’s S-Boxes with 4-bit output. The new S-Boxes were also inserted in Serpent’s architecture. This novel approach of using magic numbers and the Blocker Function worked successfully in this study. The results demonstrated an algorithm for which its S-Box is composed of a 4-bit-output that is more vulnerable to being attacked than an algorithm in which its S-Box comprises 32-bit outputs. The novel approach of using a Blocker, developed by three magic numbers and 32-bits output S-Boxes, successfully blocked the construction of DLCT and DL attacks. This approach managed to secure the Serpent algorithm installed on IoT devices against DL attacks. Full article
(This article belongs to the Special Issue Security for Connected Embedded Devices)
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