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Advances in Security for Emerging Intelligent 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: 31 May 2025 | Viewed by 10651

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Special Issue Editors

Dr. Cong Wu

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Guest Editor

Cyber Security Lab, College of Computing and Data Science, Nanyang Technological University, Singapore, Singapore
Interests: Internet of Things; cyber security; user authentication; biometrics; wireless sensing; system security; LLM security; Web3 security; biometric security; vulnerability detection and repairing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Jing Chen

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Guest Editor

School of Cyber Science and Engineering, Wuhan University, Wuhan, China
Interests: network security; cloud security; mobile security

Dr. Yebo Feng

E-Mail Website
Guest Editor

Cyber Security Lab, College of Computing and Data Science, Nanyang Technological University, Singapore, Singapore
Interests: network security; IoT security; network traffic analysis; blockchain security; decentralized finance (DeFi); DDoS defense; machine learning for security

Dr. Xianhao Chen

E-Mail Website
Guest Editor

Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China
Interests: wireless networking
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid advancement of emerging intelligent systems has brought significant benefits and complex challenges to the field of cybersecurity. These technologies, which include advanced sensors integrated with artificial intelligence and machine learning algorithms, are increasingly deployed across various sectors such as healthcare, finance, transportation, and critical infrastructure. The capabilities of these intelligent systems to autonomously collect, process, and analyze data make them vital components in modern applications. However, the complexity and interconnectedness of emerging intelligent systems also make them prime targets for sophisticated cyber-attacks, posing risks to their reliable operation across various applications and industries. Ensuring the security of these systems is essential to maintain public trust, operational reliability, and the protection of sensitive data.

This Special Issue aims to address the urgent need for robust security measures to protect emerging intelligent systems from evolving threats by showcasing the latest research and innovative solutions to enhance their security and resilience against potential cyber threats. We invite researchers and practitioners to contribute their latest findings and insights, fostering a deeper understanding and development of security and privacy measures for these systems. We seek high-quality submissions on topics including, but not limited to:

Data security for intelligent systems
Attacks and defenses in sensor network
Attacks and defenses in intelligent intrusion detection system
Attacks and defenses in blockchain systems
Attacks and defenses in decentralized ecosystem
Attacks and defenses in emerging artificial intelligent technology
Security and privacy of biometric systems
Emerging artificial intelligent technology for intelligent systems
Cyber-physical intelligent system security
Privacy-preserving techniques for intelligent systems
Privacy attacks on intelligent systems
Security of edge and fog computing
Secure integration of IoT devices
Formal analyzing of for intelligent system security
Case studies and real-world applications of intelligent system security
Policy and governance for intelligent systems
Regulatory and standardization of intelligent system security
Survey of intelligent system security

Dr. Cong Wu
Prof. Dr. Jing Chen
Dr. Yebo Feng
Dr. Xianhao Chen
Guest Editors

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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.

Keywords

intelligent systems security
cyber threat resilience
AI and ML in cybersecurity
privacy-preserving technology
blockchain and IoT security

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Published Papers (13 papers)

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Research

18 pages, 2350 KiB

Open AccessArticle

Optimized Identity Authentication via Channel State Information for Two-Factor User Verification in Information Systems

by Chuangeng Tian, Fanjia Li, Xiaomeng Liu and Juanjuan Li

Sensors 2025, 25(8), 2465; https://doi.org/10.3390/s25082465 - 14 Apr 2025

Viewed by 206

Abstract

Traditional user authentication mechanisms in information systems, such as passwords and biometrics, remain vulnerable to forgery, theft, and privacy breaches. To address these limitations, this study proposes a two-factor authentication framework that integrates Channel State Information (CSI) with conventional methods to enhance security and reliability. The proposed approach leverages unique CSI variations induced by user-specific keystroke dynamics to extract discriminative biometric features. A robust signal processing pipeline is implemented, combining Hampel filtering, Butterworth low-pass filtering, and wavelet transform threshold denoising to eliminate noise and outliers from raw CSI data. Feature extraction is further optimized through a dual-threshold moving window detection algorithm for precise activity segmentation, a subcarrier selection method to filter redundant or unstable channels, and principal component analysis (PCA) to reduce feature dimensionality while retaining 90% of critical information. For classification, a kernel support vector machine (SVM) model is trained using a randomized hyperparameter search algorithm. The SVM classifies the CSI feature patterns obtained from user-specific keystroke dynamics, which are processed by Hampel filtering, Butterworth low-pass filtering, wavelet transform threshold denoising, a dual-threshold moving window detection algorithm, a subcarrier selection method, and PCA, to achieve optimal performance. The experimental results show that the user recognition accuracy of this algorithm is 2–3% better than current algorithms. Full article

(This article belongs to the Special Issue Advances in Security for Emerging Intelligent Systems)

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15 pages, 466 KiB

Open AccessArticle

Privacy-Preserving Federated Learning Framework for Multi-Source Electronic Health Records Prognosis Prediction

by Huiya Zhao, Dehao Sui, Yasha Wang, Liantao Ma and Ling Wang

Sensors 2025, 25(8), 2374; https://doi.org/10.3390/s25082374 - 9 Apr 2025

Viewed by 327

Abstract

Secure and privacy-preserving health status representation learning has become a critical challenge in clinical prediction systems. While deep learning models require substantial high-quality data for training, electronic health records are often restricted by strict privacy regulations and institutional policies, particularly during emerging health crises. Traditional approaches to data integration across medical institutions face significant privacy and security challenges, as healthcare providers cannot directly share patient data. This work presents MultiProg, a secure federated learning framework for clinical representation learning. Our approach enables multiple medical institutions to collaborate without exchanging raw patient data, maintaining data locality while improving model performance. The framework employs a multi-channel architecture where institutions share only the low-level feature extraction layers, protecting sensitive patient information. We introduce a feature calibration mechanism that ensures robust performance even with heterogeneous feature sets across different institutions. Through extensive experiments, we demonstrate that the framework successfully enables secure knowledge sharing across institutions without compromising sensitive patient data, achieving enhanced predictive capabilities compared to isolated institutional models. Compared to state-of-the-art methods, our approach achieves the best performance across multiple datasets with statistically significant improvements. Full article

(This article belongs to the Special Issue Advances in Security for Emerging Intelligent Systems)

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17 pages, 1381 KiB

Open AccessArticle

Robust Adversarial Example Detection Algorithm Based on High-Level Feature Differences

by Hua Mu, Chenggang Li, Anjie Peng, Yangyang Wang and Zhenyu Liang

Sensors 2025, 25(6), 1770; https://doi.org/10.3390/s25061770 - 12 Mar 2025

Viewed by 439

Abstract

The threat posed by adversarial examples (AEs) to deep learning applications has garnered significant attention from the academic community. In response, various defense strategies have been proposed, including adversarial example detection. A range of detection algorithms has been developed to differentiate between benign samples and adversarial examples. However, the detection accuracy of these algorithms is significantly influenced by the characteristics of the adversarial attacks, such as attack type and intensity. Furthermore, the impact of image preprocessing on detection robustness—a common step before adversarial example generation—has been largely overlooked in prior research. To address these challenges, this paper introduces a novel adversarial example detection algorithm based on high-level feature differences (HFDs), which is specifically designed to improve robustness against both attacks and preprocessing operations. For each test image, a counterpart image with the same predicted label is randomly selected from the training dataset. The high-level features of both images are extracted using an encoder and compared through a similarity measurement model. If the feature similarity is low, the test image is classified as an adversarial example. The proposed method was evaluated for detection accuracy against four comparison methods, showing significant improvements over FS, DF, and MD, with a performance comparable to ESRM. Therefore, the subsequent robustness experiments focused exclusively on ESRM. Our results demonstrate that the proposed method exhibits superior robustness against preprocessing operations, such as downsampling and common corruptions, applied by attackers before generating adversarial examples. It is also applicable to various target models. By exploiting semantic conflicts in high-level features between clean and adversarial examples with the same predicted label, the method achieves high detection accuracy across diverse attack types while maintaining resilience to preprocessing, providing a valuable new perspective in the design of adversarial example detection algorithms. Full article

(This article belongs to the Special Issue Advances in Security for Emerging Intelligent Systems)

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14 pages, 409 KiB

Open AccessArticle

Intelligent Energy Efficiency Maximization for Wirelessly-Powered UAV-Assisted Secure Sensor Network

by Fang Xu and Xinyu Zhang

Sensors 2025, 25(5), 1534; https://doi.org/10.3390/s25051534 - 1 Mar 2025

Cited by 1 | Viewed by 481

Abstract

The rapid proliferation of Internet of Things (IoT) devices and applications has led to an increasing demand for energy-efficient and secure communication in wireless sensor networks. In this article, we firstly propose an intelligent approach to maximize the energy efficiency of the UAV in a secure sensor network with wireless power transfer (WPT). All sensors harvest energy via downlink signal and use it to transmit uplink information to the UAV. To ensure secure data transmission, the UAV needs to optimize the transmission parameters to decode received information under malicious interference from an attacker. Code Division Multiple Access (CDMA) is adopted to improve uplink communication robustness. To maximize the UAV’s energy efficiency in data collection tasks, we formulate a constrained optimization problem that jointly optimizes charging power, charging duration, and data transmission duration. Applying Deep Deterministic Policy Gradient (DDPG) algorithm, we train an action policy to dynamically determine near-optimal transmission parameters in real time. Numerical results validate the superiority of proposed intelligent approach over exhaustive search and gradient ascent techniques. This work provides some important guidelines for the design of green secure wireless-powered sensor networks. Full article

(This article belongs to the Special Issue Advances in Security for Emerging Intelligent Systems)

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19 pages, 734 KiB

Open AccessArticle

Secure and Intelligent Single-Channel Blind Source Separation via Adaptive Variational Mode Decomposition with Optimized Parameters

by Meishuang Yan, Lu Chen, Wei Hu, Zhihong Sun and Xueguang Zhou

Sensors 2025, 25(4), 1107; https://doi.org/10.3390/s25041107 - 12 Feb 2025

Viewed by 737

Abstract

Emerging intelligent systems rely on secure and efficient signal processing to ensure reliable operation in environments where there is limited prior knowledge and significant interference. Single-channel blind source separation (SCBSS) is critical for applications such as wireless communication and sensor networks, where signals are often mixed and corrupted. Variational mode decomposition (VMD) has proven effective for SCBSS, but its performance depends heavily on selecting the optimal modal component count k and quadratic penalty parameter

α

. To address this challenge, we propose a secure and intelligent SCBSS algorithm leveraging adaptive VMD optimized with Improved Particle Swarm Optimization (IPSO). The IPSO dynamically determines the optimal k and

α

parameters, enabling VMD to filter noise and create a virtual multi-channel signal. This signal is then processed using improved Fast Independent Component Analysis (IFastICA) for high-fidelity source isolation. Experiments on the RML2016.10a dataset demonstrate a 15.7% improvement in separation efficiency over conventional methods, with robust performance for BPSK and QPSK signals, achieving correlation coefficients above 0.9 and signal-to-noise ratio (SNR) improvements of up to 24.66 dB. Full article

(This article belongs to the Special Issue Advances in Security for Emerging Intelligent Systems)

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20 pages, 15263 KiB

Open AccessArticle

An Efficient Cluster-Based Mutual Authentication and Key Update Protocol for Secure Internet of Vehicles in 5G Sensor Networks

by Xinzhong Su and Youyun Xu

Sensors 2025, 25(1), 212; https://doi.org/10.3390/s25010212 - 2 Jan 2025

Viewed by 652

Abstract

The Internet of Vehicles (IoV), a key component of smart transportation systems, leverages 5G communication for low-latency data transmission, facilitating real-time interactions between vehicles, roadside units (RSUs), and sensor networks. However, the open nature of 5G communication channels exposes IoV systems to significant security threats, such as eavesdropping, replay attacks, and message tampering. To address these challenges, this paper proposes the Efficient Cluster-based Mutual Authentication and Key Update Protocol (ECAUP) designed to secure IoV systems within 5G-enabled sensor networks. The ECAUP meets the unique mobility and security demands of IoV by enabling fine-grained access control and dynamic key updates for RSUs through a factorial tree structure, ensuring both forward and backward secrecy. Additionally, physical unclonable functions (PUFs) are utilized to provide end-to-end authentication and physical layer security, further enhancing the system’s resilience against sophisticated cyber-attacks. The security of the ECAUP is formally verified using BAN Logic and ProVerif, and a comparative analysis demonstrates its superiority in terms of overhead efficiency (more than 50%) and security features over existing protocols. This work contributes to the development of secure, resilient, and efficient intelligent transportation systems, ensuring robust communication and protection in sensor-based IoV environments. Full article

(This article belongs to the Special Issue Advances in Security for Emerging Intelligent Systems)

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18 pages, 3653 KiB

Open AccessArticle

Intelligent Beam-Hopping-Based Grant-Free Random Access in Secure IoT-Oriented Satellite Networks

by Zhongliang Deng and Yicheng Liao

Sensors 2025, 25(1), 199; https://doi.org/10.3390/s25010199 - 1 Jan 2025

Viewed by 817

Abstract

This research presents an intelligent beam-hopping-based grant-free random access (GFRA) architecture designed for secure Internet of Things (IoT) communications in Low Earth Orbit (LEO) satellite networks. In light of the difficulties associated with facilitating extensive device connectivity while ensuring low latency and high reliability, we present a beam-hopping GFRA (BH-GFRA) scheme that enhances access efficiency and reduces resource collisions. Three distinct resource-hopping schemes, random hopping, group hopping, and orthogonal group hopping, are examined and utilized within the framework. This technique utilizes orthogonal resource allocation algorithms to facilitate efficient resource sharing, effectively tackling the irregular and dynamic traffic. Also, a kind of activity mechanism is proposed based on the constraints of the spatio-temporal distribution of devices. We assess the system’s performance through a thorough mathematical analysis. Furthermore, we ascertain the access delay and success rate to evaluate its capability to serve a substantial number of IoT devices under satellite–terrestrial delay and interference of massive connections. The suggested method demonstrably improves connection, stability, and access efficiency in 6G IoT satellite networks, meeting the rigorous demands of next-generation IoT applications. Full article

(This article belongs to the Special Issue Advances in Security for Emerging Intelligent Systems)

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32 pages, 5167 KiB

Open AccessArticle

Empowering Privacy Through Peer-Supervised Self-Sovereign Identity: Integrating Zero-Knowledge Proofs, Blockchain Oversight, and Peer Review Mechanism

by Junliang Liu, Zhiyao Liang and Qiuyun Lyu

Sensors 2024, 24(24), 8136; https://doi.org/10.3390/s24248136 - 20 Dec 2024

Viewed by 1494

Abstract

Frequent user data breaches and misuse incidents highlight the flaws in current identity management systems. This study proposes a blockchain-based, peer-supervised self-sovereign identity (SSI) generation and privacy protection technology. Our approach creates unique digital identities on the blockchain, enabling secure cross-domain recognition and data sharing and satisfying the essential users’ requirements for SSI. Compared to existing SSI solutions, our approach has the practical advantages of less implementation cost, ease of users’ understanding and agreement, and better possibility of being soon adopted by current society and legal systems. The key innovative technical features include (1) using a zero-knowledge proof technology to ensure data remain “usable but invisible”, mitigating data breach risks; (2) introducing a peer review mechanism among service providers to prevent excessive data requests and misuse; and (3) implementing a comprehensive multi-party supervision system to audit all involved parties and prevent misconduct. Full article

(This article belongs to the Special Issue Advances in Security for Emerging Intelligent Systems)

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12 pages, 592 KiB

Open AccessArticle

Unmanned-Aerial-Vehicle-Assisted Secure Free Space Optical Transmission in Internet of Things: Intelligent Strategy for Optimal Fairness

by Fang Xu and Mingda Dong

Sensors 2024, 24(24), 8070; https://doi.org/10.3390/s24248070 - 18 Dec 2024

Viewed by 629

Abstract

In this article, we consider an UAV (unmanned aerial vehicle)-assisted free space optical (FSO) secure communication network. Since FSO signal is impossible to detect by eavesdroppers without proper beam alignment and security authentication, a BS employs FSO technique to transfer information to multiple authenticated sensors, to improve the transmission security and reliability with the help of an UAV relay with decode and forward (DF) mode. All the sensors need to first send information to the UAV to obtain security authentication, and then the UAV forwards corresponding information to them. Successive interference cancellation (SIC) is used to decode the information received at the UAV and all authenticated sensors. With consideration of fairness, we introduce a statistical metric for evaluating the network performance, i.e., the maximum decoding outage probability for all authenticated sensors. In particular, applying an intelligent approach, we obtain a near-optimal scheme for secure transmit power allocation. With a well-trained allocation scheme, approximate closed-form expressions for optimal transmit power levels can be obtained. Through some numerical examples, we illustrate the various design trade-offs for such a system. Additionally, the validity of our approach was verified by comparing with the result from exhaustive search. In particular, the result with DRL was only

0.3 %

higher than that with exhaustive search. These results can provide some important guidelines for the fairness-aware design of UAV-assisted secure FSO communication networks. Full article

(This article belongs to the Special Issue Advances in Security for Emerging Intelligent Systems)

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23 pages, 13509 KiB

Open AccessArticle

Anomaly Detection and Remaining Useful Life Prediction for Turbofan Engines with a Key Point-Based Approach to Secure Health Management

by Yuntao Duan, Tao Zhang and Dunhuang Shi

Sensors 2024, 24(24), 8022; https://doi.org/10.3390/s24248022 - 16 Dec 2024

Cited by 3 | Viewed by 773

Abstract

Aero-engines, particularly turbofan engines, are highly complex systems that play a critical role in the aviation industry. As core components of modern aircraft, they provide the thrust necessary for flight and are essential for safe and efficient operations. However, the complexity and interconnected nature of these engines also make them vulnerable to failures and, in the context of intelligent systems, potential cyber-attacks. Ensuring the secure and reliable operation of these engines is crucial as disruptions can have significant consequences, ranging from costly maintenance issues to catastrophic accidents. The innovation of this article lies in a proposed method for obtaining key points. The research method is based on convolution and the basic shape of convolution. Through feature fusion, a self-convolution operation, a half operation, and derivative operation on the original feature data of the engine, two key points of the engine in the entire lifecycle are obtained, and these key points are analyzed in detail. Finally, the key point-based acquisition method and statistical data analysis were applied to the engine’s health planning and lifespan prediction, and the results were validated on the test set. The results indicate that the key point-based method proposed in this paper has promising prospects. Full article

(This article belongs to the Special Issue Advances in Security for Emerging Intelligent Systems)

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16 pages, 1482 KiB

Open AccessArticle

Enhancing Efficiency in Trustless Cryptography: An Optimized SM9-Based Distributed Key Generation Scheme

by Jinhong Chen, Xueguang Zhou, Wei Fu and Yihuan Mao

Sensors 2024, 24(24), 7874; https://doi.org/10.3390/s24247874 - 10 Dec 2024

Viewed by 936

Abstract

Intelligent systems are those in which behavior is determined by environmental inputs, and actions are taken to maximize the probability of achieving specific goals. Intelligent systems are widely applied across various fields, particularly in distributed intelligent systems. At the same time, due to the extensive interaction with user data, intelligent systems face significant challenges regarding security. This study proposes an optimized distributed key generation (DKG) scheme for identity-based cryptography (IBC) using the SM9 standard. Our scheme introduces a (t, n)-threshold system that functions without a trusted center, addressing the vulnerability of single points of failure in conventional key generation centers (KGCs). We reduce communication and computational demands by refining the Paillier share transformation protocol, ensuring efficient, centerless operations. The scheme’s security, validated in the existential unforgeability against adaptive chosen identity attacks (EUF-CIA) model, demonstrates its practical applicability and enhanced security for distributed intelligent systems. Full article

(This article belongs to the Special Issue Advances in Security for Emerging Intelligent Systems)

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22 pages, 13809 KiB

Open AccessArticle

Secure and Lightweight Cluster-Based User Authentication Protocol for IoMT Deployment

by Xinzhong Su and Youyun Xu

Sensors 2024, 24(22), 7119; https://doi.org/10.3390/s24227119 - 5 Nov 2024

Viewed by 853

Abstract

Authentication is considered one of the most critical technologies for the next generation of the Internet of Medical Things (IoMT) due to its ability to significantly improve the security of sensors. However, higher frequency cyber-attacks and more intrusion methods significantly increase the security risks of IoMT sensor devices, resulting in more and more patients’ privacy being threatened. Different from traditional IoT devices, sensors are generally considered to be based on low-cost hardware designs with limited storage resources; thus, authentication techniques for IoMT scenarios might not be applicable anymore. In this paper, we propose an efficient three-factor cluster-based user authentication protocol (3ECAP). Specifically, we establish the security association between the user and the sensor cluster through fine-grained access control based on Merkle, which perfectly achieves the segmentation of permission. We then demonstrate that 3ECAP can address the privilege escalation attack caused by permission segmentation. Moreover, we further analyze the security performance and communication cost using formal and non-formal security analysis, Proverif, and NS3. Simulation results demonstrated the robustness of 3ECAP against various cyber-attacks and its applicability in an IoMT environment with limited storage resources. Full article

(This article belongs to the Special Issue Advances in Security for Emerging Intelligent Systems)

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15 pages, 541 KiB

Open AccessCommunication

Improving Factuality by Contrastive Decoding with Factual and Hallucination Prompts

by Bojie Lv, Ao Feng and Chenlong Xie

Sensors 2024, 24(21), 7097; https://doi.org/10.3390/s24217097 - 4 Nov 2024

Viewed by 1502

Abstract

Large language models have demonstrated impressive capabilities in many domains. But they sometimes generate irrelevant or nonsensical text, or produce outputs that deviate from the provided input, an occurrence commonly referred to as hallucination. To mitigate this issue, we introduce a novel decoding method that incorporates both factual and hallucination prompts (DFHP). It applies contrastive decoding to highlight the disparity in output probabilities between factual prompts and hallucination prompts. Experiments on both multiple-choice and text generation tasks show that our approach significantly improves factual accuracy of large language models without additional training. On the TruthfulQA dataset, the DFHP method significantly improves factual accuracy of the LLaMA model, with an average improvement of 6.4% for the 7B, 13B, 30B, and 65B versions. Its high accuracy in factuality makes it an ideal choice for high reliability tasks like medical diagnosis and legal cases. Full article

(This article belongs to the Special Issue Advances in Security for Emerging Intelligent Systems)

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