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Data Security Approaches for Autonomous Systems, IoT, and Smart Sensing Systems

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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: 25 July 2025 | Viewed by 23697

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Special Issue Editors

Prof. Dr. Rabi N. Mahapatra

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Guest Editor

Department of Computer Science and Engineering, Texas A&M University, College Station, TX 77845, USA
Interests: Internet of Things architecture and security; ML codesign architecture; embedded system security

Dr. Amar Rasheed

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Guest Editor

Department of Computer Science, Sam Houston State University, Huntsville, TX 77340, USA
Interests: IoT; sensor network; hardware security; vehicular network security

Special Issue Information

Dear Colleagues,

Intelligent sensors, ubiquitous computing, and IoT systems are increasingly becoming an integral part of today’s infrastructure. The convergence of smart sensing and intelligent IoT systems in supporting embedded computing solutions introduces a new era for system autonomation. Technologies based on system automation and intelligent data sensing have been widely deployed in manufacturing, the automobile industry, industrial control systems (ICS), smart home monitoring systems, robotics, autonomous ground vehicles, and smart vehicle systems. Through system autonomation, complex tasks are monitored and controlled by hundreds of small sensing elements. These intelligent sensing units are capable of dynamically adjusting the overall system’s behavior. Furthermore, autonomous systems integrated into smart vehicles are incorporated with dozens of sensing units, monitoring critical subsystems such as the engine control unit, brake system, autonomous navigation system, and collision avoidance, increasing safety and enhancing the driving experience. However, with the increasing reliance on system automation for improving the quality of life, these systems contribute to a new type of vulnerability. Many of these systems were mainly designed to support reliable and robust sensing and control capabilities without the consideration of data security and system resilience against cyber threats. Autonomous systems have been exposed to a large number of security threats including sensor data modification attacks, replay attacks, denial of service (DOS) attacks, attacks on the error control algorithm, and sensor data injection attacks. Autonomous systems lack the support of reliable and efficient data encryption/decryption. Sensor data transmitted over the system are not encrypted and authenticated in such a system.

This Special Issue invites the submission of high-quality and unpublished research papers that propose novel security approaches to protect autonomous systems from potential cyber threats. The main aim of this Special Issue is to integrate, develop, and employ new data security solutions for autonomous system, IoT systems, and embedded sensor units. Theoretical and experimental works with system setup based on IoT platforms are encouraged too. Topics of interest include, but are not limited to:

Security and privacy schemes for the IoT;
Power-efficient and light-weight data encryption scheme for smart vehicles and unmanned autonomous systems;
Privacy-preserving protocols for unmanned arial vehicle systems;
Authenticated data encryption schemes for autonomous systems with controller area network (CAN);
System vulnerability and threat modeling for smart vehicle systems;
Intrusion detection system based on machine learning approaches for IoT systems.

Prof. Dr. Rabi N. Mahapatra
Dr. Amar Rasheed
Guest Editors

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Published Papers (6 papers)

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Research

26 pages, 1244 KiB

Open AccessArticle

Privacy-Preserving Detection of Tampered Radio-Frequency Transmissions Utilizing Federated Learning in LoRa Networks

by Nurettin Selcuk Senol, Mohamed Baza, Amar Rasheed and Maazen Alsabaan

Sensors 2024, 24(22), 7336; https://doi.org/10.3390/s24227336 - 17 Nov 2024

Cited by 2 | Viewed by 1099

Abstract

LoRa networks, widely adopted for low-power, long-range communication in IoT applications, face critical security concerns as radio-frequency transmissions are increasingly vulnerable to tampering. This paper addresses the dual challenges of privacy-preserving detection of tampered transmissions and the identification of unknown attacks in LoRa-based IoT networks. Leveraging Federated Learning (FL), our approach enables the detection of tampered RF transmissions while safeguarding sensitive IoT data, as FL allows model training on distributed devices without sharing raw data. We evaluated the performance of multiple FL-enabled anomaly-detection algorithms, including Convolutional Autoencoder Federated Learning (CAE-FL), Isolation Forest Federated Learning (IF-FL), One-Class Support Vector Machine Federated Learning (OCSVM-FL), Local Outlier Factor Federated Learning (LOF-FL), and K-Means Federated Learning (K-Means-FL). Using metrics such as accuracy, precision, recall, and F1-score, CAE-FL emerged as the top performer, achieving 97.27% accuracy and a balanced precision, recall, and F1-score of 0.97, with IF-FL close behind at 96.84% accuracy. Competitive performance from OCSVM-FL and LOF-FL, along with the comparable results of K-Means-FL, highlighted the robustness of clustering-based detection methods in this context. Visual analyses using confusion matrices and ROC curves provided further insights into each model’s effectiveness in detecting tampered signals. This research underscores the capability of federated learning to enhance privacy and security in anomaly detection for LoRa networks, even against unknown attacks, marking a significant advancement in securing IoT communications in sensitive applications. Full article

(This article belongs to the Special Issue Data Security Approaches for Autonomous Systems, IoT, and Smart Sensing Systems)

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26 pages, 4554 KiB

Open AccessArticle

Stability Analysis through a Stability Factor Metric for IQRF Mesh Sensor Networks Utilizing Merged Data Collection

by Gergely Sebestyen and Jozsef Kopjak

Sensors 2024, 24(15), 4977; https://doi.org/10.3390/s24154977 - 31 Jul 2024

Viewed by 1070

Abstract

This paper introduces a novel stability metric specifically developed for IQRF wireless mesh sensor networks, emphasizing flooding routing and data collection methodologies, particularly IQRF’s Fast Response Command (FRC) technique. A key feature of this metric is its ability to ensure network resilience against disruptions by effectively utilizing redundant paths in the network. This makes the metric an indispensable tool for field engineers in both the design and deployment of wireless sensor networks. Our findings provide valuable insights, demonstrating the metric’s efficacy in achieving robust and reliable network operations, especially in data collection tasks. The inclusion of redundant paths as a factor in the stability metric significantly enhances its practicality and relevance. Furthermore, this research offers practical ideas for enhancing the design and management of wireless mesh sensor networks. The stability metric uniquely assesses the resilience of data collection activities within these networks, with a focus on the benefits of redundant paths, underscoring the significance of stability in network evaluation. Full article

(This article belongs to the Special Issue Data Security Approaches for Autonomous Systems, IoT, and Smart Sensing Systems)

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39 pages, 2500 KiB

Open AccessArticle

On Evaluating Black-Box Explainable AI Methods for Enhancing Anomaly Detection in Autonomous Driving Systems

by Sazid Nazat, Osvaldo Arreche and Mustafa Abdallah

Sensors 2024, 24(11), 3515; https://doi.org/10.3390/s24113515 - 29 May 2024

Cited by 6 | Viewed by 3346

Abstract

The recent advancements in autonomous driving come with the associated cybersecurity issue of compromising networks of autonomous vehicles (AVs), motivating the use of AI models for detecting anomalies on these networks. In this context, the usage of explainable AI (XAI) for explaining the behavior of these anomaly detection AI models is crucial. This work introduces a comprehensive framework to assess black-box XAI techniques for anomaly detection within AVs, facilitating the examination of both global and local XAI methods to elucidate the decisions made by XAI techniques that explain the behavior of AI models classifying anomalous AV behavior. By considering six evaluation metrics (descriptive accuracy, sparsity, stability, efficiency, robustness, and completeness), the framework evaluates two well-known black-box XAI techniques, SHAP and LIME, involving applying XAI techniques to identify primary features crucial for anomaly classification, followed by extensive experiments assessing SHAP and LIME across the six metrics using two prevalent autonomous driving datasets, VeReMi and Sensor. This study advances the deployment of black-box XAI methods for real-world anomaly detection in autonomous driving systems, contributing valuable insights into the strengths and limitations of current black-box XAI methods within this critical domain. Full article

(This article belongs to the Special Issue Data Security Approaches for Autonomous Systems, IoT, and Smart Sensing Systems)

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30 pages, 4027 KiB

Open AccessArticle

Anomaly Detection IDS for Detecting DoS Attacks in IoT Networks Based on Machine Learning Algorithms

by Esra Altulaihan, Mohammed Amin Almaiah and Ahmed Aljughaiman

Sensors 2024, 24(2), 713; https://doi.org/10.3390/s24020713 - 22 Jan 2024

Cited by 70 | Viewed by 8111

Abstract

Widespread and ever-increasing cybersecurity attacks against Internet of Things (IoT) systems are causing a wide range of problems for individuals and organizations. The IoT is self-configuring and open, making it vulnerable to insider and outsider attacks. In the IoT, devices are designed to self-configure, enabling them to connect to networks autonomously without extensive manual configuration. By using various protocols, technologies, and automated processes, self-configuring IoT devices are able to seamlessly connect to networks, discover services, and adapt their configurations without requiring manual intervention or setup. Users’ security and privacy may be compromised by attackers seeking to obtain access to their personal information, create monetary losses, and spy on them. A Denial of Service (DoS) attack is one of the most devastating attacks against IoT systems because it prevents legitimate users from accessing services. A cyberattack of this type can significantly damage IoT services and smart environment applications in an IoT network. As a result, securing IoT systems has become an increasingly significant concern. Therefore, in this study, we propose an IDS defense mechanism to improve the security of IoT networks against DoS attacks using anomaly detection and machine learning (ML). Anomaly detection is used in the proposed IDS to continuously monitor network traffic for deviations from normal profiles. For that purpose, we used four types of supervised classifier algorithms, namely, Decision Tree (DT), Random Forest (RF), K Nearest Neighbor (kNN), and Support Vector Machine (SVM). In addition, we utilized two types of feature selection algorithms, the Correlation-based Feature Selection (CFS) algorithm and the Genetic Algorithm (GA) and compared their performances. We also utilized the IoTID20 dataset, one of the most recent for detecting anomalous activity in IoT networks, to train our model. The best performances were obtained with DT and RF classifiers when they were trained with features selected by GA. However, other metrics, such as training and testing times, showed that DT was superior. Full article

(This article belongs to the Special Issue Data Security Approaches for Autonomous Systems, IoT, and Smart Sensing Systems)

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16 pages, 5243 KiB

Open AccessArticle

Publish/Subscribe Method for Real-Time Data Processing in Massive IoT Leveraging Blockchain for Secured Storage

by Mohammadhossein Ataei, Ali Eghmazi, Ali Shakerian, Rene Landry, Jr. and Guy Chevrette

Sensors 2023, 23(24), 9692; https://doi.org/10.3390/s23249692 - 8 Dec 2023

Cited by 2 | Viewed by 5672

Abstract

In the Internet of Things (IoT) era, the surge in Machine-Type Devices (MTDs) has introduced Massive IoT (MIoT), opening new horizons in the world of connected devices. However, such proliferation presents challenges, especially in storing and analyzing massive, heterogeneous data streams in real time. In order to manage Massive IoT data streams, we utilize analytical database software such as Apache Druid version 28.0.0 that excels in real-time data processing. Our approach relies on a publish/subscribe mechanism, where device-generated data are relayed to a dedicated broker, effectively functioning as a separate server. This broker enables any application to subscribe to the dataset, promoting a dynamic and responsive data ecosystem. At the core of our data transmission infrastructure lies Apache Kafka version 3.6.1, renowned for its exceptional data flow management performance. Kafka efficiently bridges the gap between MIoT sensors and brokers, enabling parallel clusters of brokers that lead to more scalability. In our pursuit of uninterrupted connectivity, we incorporate a fail-safe mechanism with two Software-Defined Radios (SDR) called Nutaq PicoLTE Release 1.5 within our model. This strategic redundancy enhances data transmission availability, safeguarding against connectivity disruptions. Furthermore, to enhance the data repository security, we utilize blockchain technology, specifically Hyperledger Fabric, known for its high-performance attributes, ensuring data integrity, immutability, and security. Our latency results demonstrate that our platform effectively reduces latency for 100,000 devices, qualifying as an MIoT, to less than 25 milliseconds. Furthermore, our findings on blockchain performance underscore our model as a secure platform, achieving over 800 Transactions Per Second in a dataset comprising 14,000 transactions, thereby demonstrating its high efficiency. Full article

(This article belongs to the Special Issue Data Security Approaches for Autonomous Systems, IoT, and Smart Sensing Systems)

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25 pages, 3959 KiB

Open AccessArticle

CANAttack: Assessing Vulnerabilities within Controller Area Network

by Damilola Oladimeji, Amar Rasheed, Cihan Varol, Mohamed Baza, Hani Alshahrani and Abdullah Baz

Sensors 2023, 23(19), 8223; https://doi.org/10.3390/s23198223 - 2 Oct 2023

Cited by 13 | Viewed by 3773

Abstract

Current vehicles include electronic features that provide ease and convenience to drivers. These electronic features or nodes rely on in-vehicle communication protocols to ensure functionality. One of the most-widely adopted in-vehicle protocols on the market today is the Controller Area Network, popularly referred to as the CAN bus. The CAN bus is utilized in various modern, sophisticated vehicles. However, as the sophistication levels of vehicles continue to increase, we now see a high rise in attacks against them. These attacks range from simple to more-complex variants, which could have detrimental effects when carried out successfully. Therefore, there is a need to carry out an assessment of the security vulnerabilities that could be exploited within the CAN bus. In this research, we conducted a security vulnerability analysis on the CAN bus protocol by proposing an attack scenario on a CAN bus simulation that exploits the arbitration feature extensively. This feature determines which message is sent via the bus in the event that two or more nodes attempt to send a message at the same time. It achieves this by prioritizing messages with lower identifiers. Our analysis revealed that an attacker can spoof a message ID to gain high priority, continuously injecting messages with the spoofed ID. As a result, this prevents the transmission of legitimate messages, impacting the vehicle’s operations. We identified significant risks in the CAN protocol, including spoofing, injection, and Denial of Service. Furthermore, we examined the latency of the CAN-enabled system under attack, finding that the compromised node (the attacker’s device) consistently achieved the lowest latency due to message arbitration. This demonstrates the potential for an attacker to take control of the bus, injecting messages without contention, thereby disrupting the normal operations of the vehicle, which could potentially compromise safety. Full article

(This article belongs to the Special Issue Data Security Approaches for Autonomous Systems, IoT, and Smart Sensing Systems)

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