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Security of Sensor Network Systems and Circuits from a Hardware Perspective

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

Deadline for manuscript submissions: 10 April 2025 | Viewed by 15913

Special Issue Editors

Prof. Dr. Chip Hong Chang

E-Mail Website
Guest Editor
School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore
Interests: hardware security; AI security; biometric security; trustworthy sensing and hardware accelerators for post-quantum cryptography and edge computational intelligence
Dr. Wenye Liu

E-Mail Website
Guest Editor
School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore
Interests: hardware security; fault injection attack; machine learning accelerator; VLSI design
Prof. Dr. Stefan Katzenbeisser

E-Mail Website
Guest Editor
Chair of Computer Engineering, University of Passau, 94032 Passau, Germany
Interests: cryptographic protocols (design, analysis); cryptographic techniques for noisy and fuzzy data; secure critical infrastructures and railway security; physically unclonable functions; privacy enhancing technologies; watermarking; steganography and covert channels

Special Issue Information

Dear Colleagues,

Recent advances in Internet of Things (IoT) have launched a new generation of edge applications. A large amount of data, including audio, image, temperature, pressure and biometrics, need to be collected, processed and analyzed directly to a certain extent on the endpoint nodes. These emerging near-sensor and in-sensor computing paradigms mandate efficient hardware implementation for intelligence integration, amalgamating smart sensing and data analytics at the circuit and network level. As sensor data are the entry point of network systems, they are subject to more attack vectors and pose security and privacy threats to the connected systems and their users. There is a dire need to enhance the trustworthiness of intelligent sensing by integrating preventative and protective countermeasures into circuits and networks nearest to the sensors, if not directly from where the data are generated. Lightweight hardware roots of trust, embedded cryptography, efficient secure processors or co-processors and hardware-assisted authentication are promising directions in this light. Distributed intelligent sensor systems, especially artificially intelligent circuits and systems for networked computer vision applications, have also become attractive targets for physical attacks such as fault injection and side-channel attacks. Hardware-oriented local and remote attacks on distributed sensor networks and circuits are unique and require special countermeasures and protections. Securing sensor systems from a hardware perspective is therefore essential to establish reliable and trustworthy ubiquitous sensor networks (USN).

This Special Issue therefore aims to put together original research and review articles on recent advances, technologies, solutions, applications, and new challenges in the field of sensor network systems and circuits.

Potential topics include, but are not limited to:

  • Trustworthy sensing;
  • Hardware-enabled security on sensor systems or prototypes;
  • Security of in-sensor, near-sensor and approximate computing;
  • Security and privacy of smart sensor networks;
  • Secure systems, circuits and architectures;
  • Hardware security primitives;
  • Cryptographic circuits;
  • Embedded processors/co-processor for security;
  • Hardware-assisted authentication/communication;
  • Physical attacks (e.g., fault, side-channel);
  • Adversarial intrusion detection and countermeasures;
  • Emerging technologies for secure architecture and applications;
  • Design and verification of hardware for security.

Prof. Dr. Chip Hong Chang
Dr. Wenye Liu
Prof. Dr. Stefan Katzenbeisser
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.

Keywords

  • trustworthy sensing
  • hardware enabled security on sensor systems or prototypes
  • security of in-sensor, near-sensor and approximate computing
  • security and privacy of smart sensor networks
  • secure systems, circuits and architectures
  • hardware security primitives
  • cryptographic circuits
  • embedded processors/co-processor for security
  • hardware-assisted authentication/communications
  • physical attacks (e.g., fault, side-channel)
  • adversarial intrusion detection and countermeasures
  • emerging technologies for secure architecture and applications
  • design and verification of hardware for security

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Published Papers (8 papers)

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Research

Jump to: Review

18 pages, 6358 KiB  
Article
Implementation of an Image Tampering Detection System with a CMOS Image Sensor PUF and OP-TEE
by Tatsuya Oyama, Manami Hagizaki, Shunsuke Okura and Takeshi Fujino
Sensors 2024, 24(22), 7121; https://doi.org/10.3390/s24227121 - 5 Nov 2024
Viewed by 727
Abstract
Since image recognition systems use image data acquired by image sensors for analysis by AI technology, an important security issue is guaranteeing the authenticity of data transmitted from image sensors to successfully perform inference using AI. There have been reports of physical attacks [...] Read more.
Since image recognition systems use image data acquired by image sensors for analysis by AI technology, an important security issue is guaranteeing the authenticity of data transmitted from image sensors to successfully perform inference using AI. There have been reports of physical attacks on image sensor interfaces by tampering with images to cause misclassifications in AI classification results. As a countermeasure against these attacks, it is effective to add authenticity to image data with a message authentication code (MAC). For the implementation of this, it is important to have technologies for generating MAC keys on image sensors and to create an environment for secure MAC verification on the host device. For MAC key generation, we used the CIS-PUF technology, which generates MAC keys from PUF responses and random numbers from CMOS image sensor variations. For the secure MAC verification, we used TEE technology, which executes security-critical processes in an environment isolated from the normal operating system. In this study, we propose and demonstrate an image tampering detection system based on MAC verification with CIS-PUF and OP-TEE in an open portable TEE on an ARM processor. In the experiments, we demonstrated a system that computed and transmitted MAC for captured images using the CIS-PUF key and then performed MAC verification in the secure world of the OP-TEE. Full article
(This article belongs to the Special Issue Security of Sensor Network Systems and Circuits from a Hardware Perspective)
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18 pages, 18528 KiB  
Article
Data Poisoning Attack against Neural Network-Based On-Device Learning Anomaly Detector by Physical Attacks on Sensors
by Takahito Ino, Kota Yoshida, Hiroki Matsutani and Takeshi Fujino
Sensors 2024, 24(19), 6416; https://doi.org/10.3390/s24196416 - 3 Oct 2024
Viewed by 3105
Abstract
In this paper, we introduce a security approach for on-device learning Edge AIs designed to detect abnormal conditions in factory machines. Since Edge AIs are easily accessible by an attacker physically, there are security risks due to physical attacks. In particular, there is [...] Read more.
In this paper, we introduce a security approach for on-device learning Edge AIs designed to detect abnormal conditions in factory machines. Since Edge AIs are easily accessible by an attacker physically, there are security risks due to physical attacks. In particular, there is a concern that the attacker may tamper with the training data of the on-device learning Edge AIs to degrade the task accuracy. Few risk assessments have been reported. It is important to understand these security risks before considering countermeasures. In this paper, we demonstrate a data poisoning attack against an on-device learning Edge AI. Our attack target is an on-device learning anomaly detection system. The system adopts MEMS accelerometers to measure the vibration of factory machines and detect anomalies. The anomaly detector also adopts a concept drift detection algorithm and multiple models to accommodate multiple normal patterns. For the attack, we used a method in which measurements are tampered with by exposing the MEMS accelerometer to acoustic waves of a specific frequency. The acceleration data falsified by this method were trained on an anomaly detector, and the result was that the abnormal state could not be detected. Full article
(This article belongs to the Special Issue Security of Sensor Network Systems and Circuits from a Hardware Perspective)
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18 pages, 750 KiB  
Article
DTR-SHIELD: Mutual Synchronization for Protecting against DoS Attacks on the SHIELD Protocol with AES-CTR Mode
by Sang-su Lee, Jong-sik Moon, Yong-je Choi, Daewon Kim and Seungkwang Lee
Sensors 2024, 24(13), 4163; https://doi.org/10.3390/s24134163 - 26 Jun 2024
Cited by 1 | Viewed by 1089
Abstract
To enhance security in the semiconductor industry’s globalized production, the Defense Advanced Research Projects Agency (DARPA) proposed an authentication protocol under the Supply Chain Hardware Integrity for Electronics Defense (SHIELD) program. This protocol integrates a secure hardware root-of-trust, known as a dielet, into [...] Read more.
To enhance security in the semiconductor industry’s globalized production, the Defense Advanced Research Projects Agency (DARPA) proposed an authentication protocol under the Supply Chain Hardware Integrity for Electronics Defense (SHIELD) program. This protocol integrates a secure hardware root-of-trust, known as a dielet, into integrated circuits (ICs). The SHIELD protocol, combined with the Advanced Encryption Standard (AES) in counter mode, named CTR-SHIELD, targets try-and-check attacks. However, CTR-SHIELD is vulnerable to desynchronization attacks on its counter blocks. To counteract this, we introduce the DTR-SHIELD protocol, where DTR stands for double counters. DTR-SHIELD addresses the desynchronization issue by altering the counter incrementation process, which previously solely relied on truncated serial IDs. Our protocol adds a new AES encryption step and requires the dielet to transmit an additional 100 bits, ensuring more robust security through active server involvement and message verification. Full article
(This article belongs to the Special Issue Security of Sensor Network Systems and Circuits from a Hardware Perspective)
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15 pages, 525 KiB  
Article
Deep Neural Network Quantization Framework for Effective Defense against Membership Inference Attacks
by Azadeh Famili and Yingjie Lao
Sensors 2023, 23(18), 7722; https://doi.org/10.3390/s23187722 - 7 Sep 2023
Cited by 2 | Viewed by 1826
Abstract
Machine learning deployment on edge devices has faced challenges such as computational costs and privacy issues. Membership inference attack (MIA) refers to the attack where the adversary aims to infer whether a data sample belongs to the training set. In other words, user [...] Read more.
Machine learning deployment on edge devices has faced challenges such as computational costs and privacy issues. Membership inference attack (MIA) refers to the attack where the adversary aims to infer whether a data sample belongs to the training set. In other words, user data privacy might be compromised by MIA from a well-trained model. Therefore, it is vital to have defense mechanisms in place to protect training data, especially in privacy-sensitive applications such as healthcare. This paper exploits the implications of quantization on privacy leakage and proposes a novel quantization method that enhances the resistance of a neural network against MIA. Recent studies have shown that model quantization leads to resistance against membership inference attacks. Existing quantization approaches primarily prioritize performance and energy efficiency; we propose a quantization framework with the main objective of boosting the resistance against membership inference attacks. Unlike conventional quantization methods whose primary objectives are compression or increased speed, our proposed quantization aims to provide defense against MIA. We evaluate the effectiveness of our methods on various popular benchmark datasets and model architectures. All popular evaluation metrics, including precision, recall, and F1-score, show improvement when compared to the full bitwidth model. For example, for ResNet on Cifar10, our experimental results show that our algorithm can reduce the attack accuracy of MIA by 14%, the true positive rate by 37%, and F1-score of members by 39% compared to the full bitwidth network. Here, reduction in true positive rate means the attacker will not be able to identify the training dataset members, which is the main goal of the MIA. Full article
(This article belongs to the Special Issue Security of Sensor Network Systems and Circuits from a Hardware Perspective)
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18 pages, 1741 KiB  
Article
A Circuit-Level Solution for Secure Temperature Sensor
by Mashrafi Alam Kajol, Mohammad Mezanur Rahman Monjur and Qiaoyan Yu
Sensors 2023, 23(12), 5685; https://doi.org/10.3390/s23125685 - 18 Jun 2023
Cited by 2 | Viewed by 1945
Abstract
Temperature sensors play an important role in modern monitoring and control applications. When more and more sensors are integrated into internet-connected systems, the integrity and security of sensors become a concern and cannot be ignored anymore. As sensors are typically low-end devices, there [...] Read more.
Temperature sensors play an important role in modern monitoring and control applications. When more and more sensors are integrated into internet-connected systems, the integrity and security of sensors become a concern and cannot be ignored anymore. As sensors are typically low-end devices, there is no built-in defense mechanism in sensors. It is common that system-level defense provides protection against security threats on sensors. Unfortunately, high-level countermeasures do not differentiate the root of cause and treat all anomalies with system-level recovery processes, resulting in high-cost overhead on delay and power consumption. In this work, we propose a secure architecture for temperature sensors with a transducer and a signal conditioning unit. The proposed architecture estimates the sensor data with statistical analysis and generates a residual signal for anomaly detection at the signal conditioning unit. Moreover, complementary current–temperature characteristics are exploited to generate a constant current reference for attack detection at the transducer level. Anomaly detection at the signal conditioning unit and attack detection at the transducer unit make the temperature sensor attack resilient to intentional and unintentional attacks. Simulation results show that our sensor is capable of detecting an under-powering attack and analog Trojan from a significant signal vibration in the constant current reference. Furthermore, the anomaly detection unit detects anomalies at the signal conditioning level from the generated residual signal. The proposed detection system is resilient against any intentional and unintentional attacks, with a detection rate of 97.73%. Full article
(This article belongs to the Special Issue Security of Sensor Network Systems and Circuits from a Hardware Perspective)
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21 pages, 951 KiB  
Article
A PUF-Based Key Storage Scheme Using Fuzzy Vault
by Jinrong Yang, Shuai Chen and Yuan Cao
Sensors 2023, 23(7), 3476; https://doi.org/10.3390/s23073476 - 26 Mar 2023
Cited by 2 | Viewed by 2316
Abstract
Physical Unclonable Functions (PUFs) are considered attractive low-cost security anchors in the key generation scheme. The helper data algorithm is usually used to transform the fuzzy responses extracted from PUF into a reproducible key. The generated key can be used to encrypt secret [...] Read more.
Physical Unclonable Functions (PUFs) are considered attractive low-cost security anchors in the key generation scheme. The helper data algorithm is usually used to transform the fuzzy responses extracted from PUF into a reproducible key. The generated key can be used to encrypt secret data in traditional security schemes. In contrast, this work shows that the fuzzy responses of both weak and strong PUFs can be used to secretly store the important data (e.g., the distributed keys) directly by an error-tolerant algorithm, Fuzzy Vault, without the traditional encryption algorithm and helper data scheme. The locking and unlocking methods of our proposal are designed to leverage the feature of weak and strong PUFs relatively. For the strong PUFs, our proposal is a new train of thought about how to leverage the advantage of strong PUFs (exponential number of challenge–response pairs) when used in the field. The evaluation was performed on existing weak PUF and strong PUF designs. The unlocking rate and runtime are tested under different parameters and environments. The test results demonstrate that our proposal can reach a 100% unlocking rate by parameter adjustment with less than 1 second of locking time and a few seconds of unlocking time. Finally, the tradeoff between security, reliability, and overhead of the new proposal is discussed. Full article
(This article belongs to the Special Issue Security of Sensor Network Systems and Circuits from a Hardware Perspective)
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Review

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20 pages, 4362 KiB  
Review
Memristive True Random Number Generator for Security Applications
by Xianyue Zhao, Li-Wei Chen, Kefeng Li, Heidemarie Schmidt, Ilia Polian and Nan Du
Sensors 2024, 24(15), 5001; https://doi.org/10.3390/s24155001 - 2 Aug 2024
Cited by 1 | Viewed by 1243
Abstract
This study explores memristor-based true random number generators (TRNGs) through their evolution and optimization, stemming from the concept of memristors first introduced by Leon Chua in 1971 and realized in 2008. We will consider memristor TRNGs coming from various entropy sources for producing [...] Read more.
This study explores memristor-based true random number generators (TRNGs) through their evolution and optimization, stemming from the concept of memristors first introduced by Leon Chua in 1971 and realized in 2008. We will consider memristor TRNGs coming from various entropy sources for producing high-quality random numbers. However, we must take into account both their strengths and weaknesses. The comparison with CMOS-based TRNGs will serve as an illustration that memristor TRNGs stand out due to their simpler circuits and lower power consumption— thus leading us into a case study involving electroless YMnO3 (YMO) memristors as TRNG entropy sources that demonstrate good security properties by being able to produce unpredictable random numbers effectively. The end of our analysis sees us pinpointing challenges: post-processing algorithm optimization coupled with ensuring reliability over time for memristor-based TRNGs aimed at next-generation security applications. Full article
(This article belongs to the Special Issue Security of Sensor Network Systems and Circuits from a Hardware Perspective)
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34 pages, 594 KiB  
Review
A Review on Immune-Inspired Node Fault Detection in Wireless Sensor Networks with a Focus on the Danger Theory
by Dominik Widhalm , Karl M. Goeschka  and Wolfgang Kastner 
Sensors 2023, 23(3), 1166; https://doi.org/10.3390/s23031166 - 19 Jan 2023
Cited by 5 | Viewed by 2587
Abstract
The use of fault detection and tolerance measures in wireless sensor networks is inevitable to ensure the reliability of the data sources. In this context, immune-inspired concepts offer suitable characteristics for developing lightweight fault detection systems, and previous works have shown promising results. [...] Read more.
The use of fault detection and tolerance measures in wireless sensor networks is inevitable to ensure the reliability of the data sources. In this context, immune-inspired concepts offer suitable characteristics for developing lightweight fault detection systems, and previous works have shown promising results. In this article, we provide a literature review of immune-inspired fault detection approaches in sensor networks proposed in the last two decades. We discuss the unique properties of the human immune system and how the found approaches exploit them. With the information from the literature review extended with the findings of our previous works, we discuss the limitations of current approaches and consequent future research directions. We have found that immune-inspired techniques are well suited for lightweight fault detection, but there are still open questions concerning the effective and efficient use of those in sensor networks. Full article
(This article belongs to the Special Issue Security of Sensor Network Systems and Circuits from a Hardware Perspective)
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