Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.2
3.5
Journals
Sensors
Special Issues
AI, Machine Learning (ML), and Large Language Models (LLMs) for...
sensors-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

AI, Machine Learning (ML), and Large Language Models (LLMs) for Cybersecurity in Sensor Networks

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

Deadline for manuscript submissions: 25 November 2026 | Viewed by 4129

Special Issue Editor

Dr. Robin Chataut

E-Mail Website
Guest Editor
College of Science and Engineering, Texas Christian University, Fort Worth, TX 76129, USA
Interests: AI; ML; LLMs; cybersecurity; NextGen Netoworks; IoT; sensor network
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The increasing sophistication of cyber threats necessitates advanced security solutions driven by Artificial Intelligence (AI), Machine Learning (ML), and Large Language Models (LLMs). This Special Issue explores the latest research in AI-driven cybersecurity, including threat detection, intrusion prevention, anomaly detection, and automated response mechanisms. By leveraging AI and LLMs, researchers and practitioners can develop more adaptive and intelligent security frameworks to combat evolving cyber risks.

This Special Issue invites contributions related to AI-enhanced security strategies, deep learning applications, adversarial machine learning, and the ethical considerations surrounding AI in cybersecurity. Topics include real-time phishing detection, malware analysis, network security, and privacy-preserving AI techniques. This Special Issue aims to provide a comprehensive overview of cutting-edge advancements in AI-powered cybersecurity solutions, aligning with the scope of Sensors.

Dr. Robin Chataut
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

  • AI for cybersecurity
  • machine learning in security
  • large language models (LLMs)
  • intrusion detection and prevention
  • phishing and malware detection
  • adversarial AI in cybersecurity
  • privacy-preserving AI techniques
  • behavioral anomaly detection
  • cyber threat intelligence
  • automated security response

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

22 pages, 1730 KB  
Article
Toward a Hybrid Intrusion Detection Framework for IIoT Using a Large Language Model
by Musaad Algarni, Mohamed Y. Dahab, Abdulaziz A. Alsulami, Badraddin Alturki and Raed Alsini
Sensors 2026, 26(4), 1231; https://doi.org/10.3390/s26041231 - 13 Feb 2026
Viewed by 637
Abstract
The widespread connectivity of the Industrial Internet of Things (IIoT) improves the efficiency and functionality of connected devices. However, it also raises serious concerns about cybersecurity threats. Implementing an effective intrusion detection system (IDS) for IIoT is challenging due to heterogeneous data, high [...] Read more.
The widespread connectivity of the Industrial Internet of Things (IIoT) improves the efficiency and functionality of connected devices. However, it also raises serious concerns about cybersecurity threats. Implementing an effective intrusion detection system (IDS) for IIoT is challenging due to heterogeneous data, high feature dimensionality, class imbalance, and the risk of data leakage during evaluation. This paper presents a leakage-safe hybrid intrusion detection framework that combines text-based and numerical network flow features in an IIoT environment. Each network flow is converted into a short text description and encoded using a frozen Large Language Model (LLM) called the Bidirectional Encoder Representations from Transformers (BERT) model to obtain fixed semantic embeddings, while numerical traffic features are standardized in parallel. To improve class separation, class prototypes are computed in Principal Component Analysis (PCA) space, and cosine similarity scores for these prototypes are added to the feature set. Class imbalance is handled only in the training data using the Synthetic Minority Over-sampling Technique (SMOTE). A Random Forest (RF) is used to select the top features, followed by a Histogram-based Gradient Boosting (HGB) classifier for final prediction. The proposed framework is evaluated on the Edge-IIoTset and ToN_IoT datasets and achieves promising results. Empirically, the framework attains 98.19% accuracy on Edge-IIoTset and 99.15% accuracy on ToN_IoT, indicating robust, leakage-safe performance. Full article
(This article belongs to the Special Issue AI, Machine Learning (ML), and Large Language Models (LLMs) for Cybersecurity in Sensor Networks)
►▼ Show Figures

Figure 1

12 pages, 1455 KB  
Article
Performance Analysis of LSTM, GRU and Hybrid LSTM–GRU Model for Detecting GPS Spoofing Attacks
by Umur Kuriş and Özgür Can Turna
Sensors 2026, 26(4), 1111; https://doi.org/10.3390/s26041111 - 9 Feb 2026
Viewed by 589
Abstract
The exposure of Unmanned Aerial Vehicles (UAVs) to Global Positioning System (GPS) spoofing attacks constitutes a major cybersecurity challenge. In this work, we conduct a comparative performance analysis of LSTM, GRU, and sequential LSTM–GRU hybrid deep learning models for the detection of GPS [...] Read more.
The exposure of Unmanned Aerial Vehicles (UAVs) to Global Positioning System (GPS) spoofing attacks constitutes a major cybersecurity challenge. In this work, we conduct a comparative performance analysis of LSTM, GRU, and sequential LSTM–GRU hybrid deep learning models for the detection of GPS spoofing attacks. The ‘UAV Attack’ dataset was preprocessed, and the 11 most significant features were selected using correlation and mutual information algorithms. The models were evaluated using a robust 5-fold cross-validation framework. A combination of 99.31% accuracy, 96.98% recall, and a 97.47% F1-score was achieved by the LSTM–GRU hybrid model, distinguishing it as the leading performer in the experimental study. The LSTM model achieved the highest precision, with a value of 98.49%. ROC curves and AUC values confirmed that the classification performance of all models was close to perfect for the simulated dataset. The findings indicate that deep-learning-based models incorporating the hybrid LSTM–GRU architectures provide an effective and reliable approach designed to identify GPS-spoofing threats affecting UAVs. Full article
(This article belongs to the Special Issue AI, Machine Learning (ML), and Large Language Models (LLMs) for Cybersecurity in Sensor Networks)
►▼ Show Figures

Figure 1

21 pages, 2458 KB  
Article
STS-AT: A Structured Tensor Flow Adversarial Training Framework for Robust Intrusion Detection
by Juntong Zhu, Zhihao Chen, Rong Cong, Hongyu Sun and Yanhua Dong
Sensors 2026, 26(2), 536; https://doi.org/10.3390/s26020536 - 13 Jan 2026
Viewed by 505
Abstract
Network intrusion detection is a key technology for ensuring cybersecurity. However, current methods face two major challenges: reliance on manual feature engineering, which leads to the loss of discriminative information, and the vulnerability of deep learning models to adversarial sample attacks. To address [...] Read more.
Network intrusion detection is a key technology for ensuring cybersecurity. However, current methods face two major challenges: reliance on manual feature engineering, which leads to the loss of discriminative information, and the vulnerability of deep learning models to adversarial sample attacks. To address these issues, this paper proposes STS-AT, a novel network intrusion detection method that integrates structured tensors with adversarial training. The method consists of three core components: first, structured tensor encoding, which fully converts raw hexadecimal traffic into a numerical representation; second, a hierarchical deep learning model that combines CNN and LSTM networks to simultaneously learn spatial and temporal features of the traffic; third, a multi-strategy adversarial training method that enhances model robustness by adaptively adjusting the mix of adversarial samples in different training phases. Experiments on the CICIDS2017 dataset show that the proposed method achieves an accuracy of 99.6% in normal traffic classification, significantly outperforming classical machine learning baselines such as Random Forest (93.1%) and Support Vector Machine (84.7%). Crucially, under various adversarial attacks (FGSM, PGD, and DeepFool), the accuracy of an undefended model drops to as low as 24.4%, whereas after multi-strategy adversarial training, the defense accuracy rises above 96.8%. Meanwhile, the total training time is reduced by approximately 67.6%. These results verify that structured tensor encoding effectively preserves original traffic information, the hierarchical model achieves comprehensive feature learning, and multi-strategy adversarial training significantly improves training efficiency while ensuring robust defense effectiveness. Full article
(This article belongs to the Special Issue AI, Machine Learning (ML), and Large Language Models (LLMs) for Cybersecurity in Sensor Networks)
►▼ Show Figures

Figure 1

22 pages, 2460 KB  
Article
AI-Driven Cybersecurity in IoT: Adaptive Malware Detection and Lightweight Encryption via TRIM-SEC Framework
by Ibrahim Mutambik
Sensors 2025, 25(22), 7072; https://doi.org/10.3390/s25227072 - 19 Nov 2025
Cited by 2 | Viewed by 1509
Abstract
The explosive growth in Internet of Things (IoT) technologies has given rise to significant security concerns, especially with the emergence of sophisticated and zero-day malware attacks. Conventional malware detection methods based on static or dynamic analysis often fail to meet the real-time operational [...] Read more.
The explosive growth in Internet of Things (IoT) technologies has given rise to significant security concerns, especially with the emergence of sophisticated and zero-day malware attacks. Conventional malware detection methods based on static or dynamic analysis often fail to meet the real-time operational needs and limited-resource constraints typical of IoT systems. This paper proposes TRIM-SEC (Transformer-Integrated Malware Security and Encryption for IoT), a lightweight and scalable framework that unifies intelligent threat detection with secure data transmission. The framework begins with Autoencoder-Based Feature Denoising (AEFD) to eliminate noise and enhance input quality, followed by Principal Component Analysis (PCA) for efficient dimensionality reduction. Malware classification is performed using a Transformer-Augmented Neural Network (TANN), which leverages multi-head self-attention to capture both contextual and temporal dependencies, enabling accurate detection of diverse threats such as Zero-Day, botnets, and zero-day exploits. For secure communication, TRIM-SEC incorporates Lightweight Elliptic Curve Cryptography (LECC), enhanced with Particle Swarm Optimization (PSO) to generate cryptographic keys with minimal computational burden. The framework is rigorously evaluated against advanced baselines, including LSTM-based IDS, CNN-GRU hybrids, and blockchain-enhanced security models. Experimental results show that TRIM-SEC delivers higher detection accuracy, fewer false alarms, and reduced encryption latency, which makes it well-suited for real-time operation in smart IoT ecosystems. Its balanced integration of detection performance, cryptographic strength, and computational efficiency positions TRIM-SEC as a promising solution for securing next-generation IoT environments. Full article
(This article belongs to the Special Issue AI, Machine Learning (ML), and Large Language Models (LLMs) for Cybersecurity in Sensor Networks)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop