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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 December 2024 | Viewed by 7869

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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

Manuscript Submission Information

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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.

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Research

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 2 | Viewed by 1530

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 - 08 Dec 2023

Cited by 1 | Viewed by 4213

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 - 02 Oct 2023

Cited by 4 | Viewed by 1751

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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