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Sensors
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Security, Privacy and Threat Detection in Sensor Networks
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Security, Privacy and Threat Detection in Sensor Networks

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

Deadline for manuscript submissions: 15 November 2026 | Viewed by 9019

Special Issue Editor

Prof. Dr. Leandros A. Maglaras

E-Mail Website
Guest Editor
School of Computer Science and Informatics, De Montfort University, Leicester LE1 9BH, UK
Interests: IoT security and privacy; intrusion detection systems; incident response
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We invite researchers and practitioners to submit papers for a Special Issue on "Security, Privacy and Threat Detection in Sensor Networks". With the increasing deployment of sensor networks in critical applications, ensuring their security and privacy has become more vital than ever. This Special Issue aims to explore innovative solutions to address the unique challenges faced by sensor networks, including securing communication, ensuring data privacy, and detecting and mitigating threats in resource-constrained environments.

We are particularly interested in papers that focus on cutting-edge topics such as post-quantum cryptography, 5G/6G communications, and the use of AI for threat detection and network analysis. Contributions on cryptographic techniques resistant to quantum computing, efficient and scalable security protocols, and privacy-preserving methods tailored to sensor networks are highly encouraged. Additionally, we welcome research that investigates the potential of LLMs in enhancing security analytics, automating threat detection, and providing intelligent network monitoring.

We also invite submissions on novel, lightweight solutions that can operate effectively within the limitations of sensor nodes, such as limited computational power and energy constraints. Papers that address real-world applications and emerging trends in securing sensor networks will be given priority. This Special Issue aims to foster advancements that will strengthen the security and privacy of sensor networks in diverse fields.

Prof. Dr. Leandros Maglaras
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 250 words) can be sent to the Editorial Office for assessment.

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

  • post-quantum cryptography
  • lightweight security solutions
  • sensor network privacy
  • threat detection
  • large language models (LLMs)
  • industrial Internet of Things

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Published Papers (3 papers)

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Research

34 pages, 3733 KB  
Article
SSDBFAN: Scalable and Secure Cluster-Based Data Aggregation with Blockchain for Flying Ad Hoc Networks
by Sufian Al Majmaie, Ghazal Ghajari, Niraj Prasad Bhatta, Mohamed I. Ibrahem and Fathi Amsaad
Sensors 2026, 26(9), 2585; https://doi.org/10.3390/s26092585 - 22 Apr 2026
Viewed by 334
Abstract
Mobile Unmanned Aerial Vehicles (UAVs) forming Flying Ad Hoc Networks (FANETs) offer promising applications, but dynamic network structures, limited resources, and potential single points of failure create security challenges. While cluster-based data aggregation, where data is collected and combined at Cluster Heads (CHs) [...] Read more.
Mobile Unmanned Aerial Vehicles (UAVs) forming Flying Ad Hoc Networks (FANETs) offer promising applications, but dynamic network structures, limited resources, and potential single points of failure create security challenges. While cluster-based data aggregation, where data is collected and combined at Cluster Heads (CHs) before transmission, improves efficiency, traditional techniques can compromise data privacy. This paper introduces SSDBFAN, a scalable and secure cluster-based data aggregation framework for Flying Ad Hoc Networks (FANETs). The proposed approach integrates the Frilled Lizard Optimization Algorithm (FLOA) for efficient cluster head selection with blockchain technology and post-quantum cryptographic techniques, including lattice-based homomorphic encryption and the Chinese Remainder Theorem, to ensure privacy-preserving data aggregation. Additionally, a hybrid online/offline signature mechanism is employed to achieve secure and efficient authentication with reduced computational overhead. The performance of the proposed framework is evaluated using NS-3 simulations under varying network sizes. Experimental results demonstrate that SSDBFAN significantly improves communication efficiency, reduces computational cost, and enhances network stability compared to existing schemes. Furthermore, scalability analysis with up to 500 UAV nodes confirms that the proposed framework effectively controls blockchain overhead, including bandwidth consumption, consensus latency, and storage requirements. Comparative evaluation with existing optimization algorithms shows that FLOA achieves superior performance in terms of cluster stability, delay, and throughput. These results validate the effectiveness of SSDBFAN as a scalable and security-aware solution for large-scale FANET environments. Full article
(This article belongs to the Special Issue Security, Privacy and Threat Detection in Sensor Networks)
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42 pages, 16346 KB  
Article
LCSMC-Net: Lightweight CAN Intrusion Detection via Separable Multiscale Convolution and Attention
by Mengdi Hou, Bitie Lan, Chenghua Tang and Jianbo Huang
Sensors 2026, 26(4), 1399; https://doi.org/10.3390/s26041399 - 23 Feb 2026
Viewed by 818
Abstract
The Controller Area Network (CAN) protocol lacks native authentication mechanisms, exposing modern vehicles to critical security threats. While deep learning-based intrusion detection systems show promise, existing solutions require computational resources far exceeding automotive-grade microcontroller constraints, hindering practical embedded deployment. This paper proposes LCSMC-Net, [...] Read more.
The Controller Area Network (CAN) protocol lacks native authentication mechanisms, exposing modern vehicles to critical security threats. While deep learning-based intrusion detection systems show promise, existing solutions require computational resources far exceeding automotive-grade microcontroller constraints, hindering practical embedded deployment. This paper proposes LCSMC-Net, an ultra-lightweight neural architecture for resource-constrained CAN intrusion detection. The framework integrates three innovations: (1) Separable Multiscale Convolution Lite (SMC-Lite) blocks capturing multitemporal attack patterns with minimal parameters; (2) Lightweight Channel-Temporal Attention (LCTA) achieving linear O(N) complexity through adaptive pruning; and (3) 6-dimensional CAN-optimized features exploiting protocol-specific characteristics for aggressive compression. The framework employs Bayesian hyperparameter optimization and knowledge distillation for systematic model compression. Extensive experiments on CAN and CAN-FD datasets demonstrate that LCSMC-Net achieves 99.89% accuracy with only 9401 parameters and 2.84M FLOPs, outperforming existing solutions while meeting real-time constraints of automotive embedded systems, providing a viable edge AI deployment solution. Full article
(This article belongs to the Special Issue Security, Privacy and Threat Detection in Sensor Networks)
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44 pages, 4528 KB  
Article
Beyond the Leak: Analyzing the Real-World Exploitation of Stolen Credentials Using Honeypots
by Matej Rabzelj and Urban Sedlar
Sensors 2025, 25(12), 3676; https://doi.org/10.3390/s25123676 - 12 Jun 2025
Cited by 2 | Viewed by 7205
Abstract
This study presents one of the most extensive analyses of the lifecycle of leaked authentication credentials to date, bridging the gap between database breaches and real-world cyberattacks. We analyze over 27 billion leaked credentials—nearly 4 billion unique—using a sophisticated data filtering and normalization [...] Read more.
This study presents one of the most extensive analyses of the lifecycle of leaked authentication credentials to date, bridging the gap between database breaches and real-world cyberattacks. We analyze over 27 billion leaked credentials—nearly 4 billion unique—using a sophisticated data filtering and normalization pipeline to handle breach inconsistencies. Following this analysis, we deploy a distributed sensor network of 39 honeypots running 14 unique services across 9 networks over a one-year-long experiment, capturing one of the most comprehensive authentication datasets in the literature. We analyze leaked credentials, SSH and Telnet session data, and HTTP authentication requests for their composition, characteristics, attack patterns, and occurrence. We comparatively assess whether credentials from leaks surface in real-world attacks. We observe a significant overlap of honeypot logins with common password wordlists (e.g., Nmap, John) and defaultlists (e.g., Piata, Mirai), and limited overlaps between leaked credentials, logins, and dictionaries. We examine generative algorithms (e.g., keywalk patterns, hashcat rules), finding they are widely used by users but not attackers—unless included in wordlists. Our analyses uncover unseen passwords and methods likely designed to detect honeypots, highlighting an adversarial arms race. Our findings offer critical insights into password reuse, mutation, and attacker strategies, with implications for authentication security, attack detection, and digital forensics. Full article
(This article belongs to the Special Issue Security, Privacy and Threat Detection in Sensor Networks)
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