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Intelligent Sensors for Security and Attack Detection

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

Deadline for manuscript submissions: 25 February 2027 | Viewed by 7564

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Editors

Prof. Dr. Frederick Sheldon

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Guest Editor

Department of Computer Science, College of Engineering, University of Idaho, Moscow, ID 83844, USA
Interests: information assurance and security; IoT; privacy protection; cybersecurity

Dr. Mohammad Ashrafuzzaman

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Guest Editor

Computer Science and Software Engineering, University of Wisconsin, Platteville, WI 53818, USA
Interests: cybersecurity; privacy protection; attack detection

Dr. Bander Ali Saleh Al-Rimy

E-Mail Website
Guest Editor

Department of Computer Science, University of Portsmouth, Portsmouth PO1 3HE, UK
Interests: malware analysis and intrusion detection; IoT/network security and reliability; cyber threat intelligence; privacy and data protection; computer forensics; blockchain; data science; AI; machine learning
Special Issues, Collections and Topics in MDPI journals

Dr. Ananth Jillepalli

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Guest Editor

College of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99164, USA
Interests: cybersecurity; computing education; data science; machine learning

Mohammed Almutairi

E-Mail Website
Guest Editor Assistant

1. Department of Computer Science, College of Engineering, University of Idaho, Moscow, ID 83844, USA
2. Applied College, University of Hafr Al Batin, Hafar Al Batin 39923, Saudi Arabia
Interests: cybersecurity; IoT; cloud security

Special Issue Information

Dear Colleagues,

The rapid evolution of intelligent sensors has transformed the landscape of modern sensing systems, enabling real-time data processing, adaptive decision-making, and secure communication across a wide range of applications. These sensors are increasingly deployed in environments such as smart cities, healthcare systems, industrial automation, transportation networks, and critical infrastructure, where security and reliability are paramount.

This Special Issue aims to collate cutting-edge research on the design, development, and deployment of intelligent sensors for security and attack detection. We welcome contributions that explore novel sensing architectures, AI- and ML-based detection algorithms, privacy-preserving mechanisms, and secure communication protocols. Submissions may address both theoretical advancements and practical implementations, including case studies and experimental validations.

Potential topics include, but are not limited to, the following:

Intelligent sensor architectures for threat detection;
AI- and ML-based intrusion, malware, and anomaly detection;
Trustworthy and Explainable AI (XAI) for sensor-based security;
Privacy-preserving sensing and secure data transmission;
Intelligent sensors in IoT and edge computing environments;
Sensor fusion for enhanced situational awareness;
Real-time monitoring and adaptive response systems;
Secure communication protocols for sensor networks;
Applications in healthcare, transportation, and industrial automation;
Experimental validation and case studies of intelligent sensor systems;
Adversarial attacks on intelligent sensors;
Securing Industrial Control Systems (ICS): a focus on SCADA and Distributed Control System (DCS) integrity;
Implementing zero trust architecture across converged IT and OT networks;
Shifting left: proactive threat hunting in Cyber-Physical Systems (CPS);
Securing Critical Infrastructure and Industrial Control Systems (ICS/OT) with intelligent sensors;
Not limited to these topics but within the general realm of security and cyberspace affecting critical infrastructure within IT and/or OT.

Prof. Dr. Frederick Sheldon
Dr. Mohammad Ashrafuzzaman
Dr. Bander Ali Saleh Al-Rimy
Dr. Ananth Jillepalli
Guest Editors

Mohammed Almutairi
Guest Editor Assistant

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-anonymized 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

intelligent sensors
security
attack detection
anomaly detection
IoT security
sensor networks
adversarial machine learning
privacy protection
malware analysis and detection
smart infrastructure

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Published Papers (5 papers)

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Research

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22 pages, 510 KB

Open AccessArticle

Attack-Aware Secure Sensor Transmission Using Pinching- Antenna Systems and Trust-Guided Cooperative Jamming

by Shen Qian, Zhao Li and Meng Cheng

Sensors 2026, 26(17), 5331; https://doi.org/10.3390/s26175331 - 22 Aug 2026

Abstract

Intelligent sensor networks are increasingly deployed in security-critical environments, where confidential sensing data may be forwarded through low-security relay sensors that are vulnerable to compromise, misuse, or internal eavesdropping. This paper investigates attack-aware secure sensor transmission in a sensor network enabled by a pinching-antenna system (PASS) with a potentially compromised relay sensor. The relay sensor assists data forwarding from a source sensor to a destination fusion center, but its received signal may also cause internal information leakage. To mitigate this threat, a trust-aware cooperative jamming framework is proposed, in which a cooperative security node generates artificial noise, feeds it into the PASS waveguide, and radiates it through selected pinching antennas toward the untrusted relay while limiting interference leakage to the legitimate fusion center. A trust-aware cooperation coefficient is introduced to characterize the security-response capability of the cooperative security node, and a candidate-set-aided PASS activation and jamming-power allocation algorithm is developed for secure sensing-data delivery. Reliable-and-secure probability, secrecy outage probability, and attack-aware secure delivery probability are adopted to jointly evaluate data reliability, confidentiality, and relay-attack resilience. Numerical results show that the proposed scheme improves secure transmission performance compared with fixed-antenna jamming, random PASS activation, trust-unaware PASS, and no-jamming benchmarks. The results also indicate that a detection-triggered PASS response can maintain a higher secure delivery probability as the relay attack probability increases. Full article

(This article belongs to the Special Issue Intelligent Sensors for Security and Attack Detection)

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42 pages, 6187 KB

Open AccessArticle

TL-RL-FusionNet: Reinforcement Learning-Guided Residual MLP with Fused CNN Embeddings for Efficient and Adaptive Ransomware Detection

by Jannatul Ferdous, Rafiqul Islam, Arash Mahboubi and Md Zahidul Islam

Sensors 2026, 26(15), 4775; https://doi.org/10.3390/s26154775 - 27 Jul 2026

Viewed by 338

Abstract

Ransomware detection remains challenging because modern variants exhibit diverse, elusive, and partly benign behaviors and can propagate rapidly across interconnected enterprises and sensor-enabled cyber-physical systems, causing cascading operational failures. These characteristics undermine signature-based and static-detection methods. Although machine learning has improved detection, many approaches still rely on fixed objectives that weight samples uniformly, limiting their adaptation to heterogeneity and overlaps between ransomware and benign activities. To address this challenge, we introduce TL-RL-FusionNet, a reinforcement learning (RL)-guided hybrid framework that combines dual transfer learning (TL) backbones, EfficientNetB0 and InceptionV3, with a lightweight residual multi-Layer perceptron (MLP) classifier. The framework converts sandbox reports into RGB grids, extracts features using frozen CNN backbone networks, and fuses embeddings for classification. Training is guided by a tabular Q-learning sample-weighting agent, formulated as a per-sample bandit over discrete weight actions. To prevent cross-fold information leakage, the Q-table is freshly initialized in each cross-validation fold and updated only using the fold-local training partition, whereas the held-out fold is used for the final evaluation. The framework was evaluated using two datasets. On our dataset, TL-RL-FusionNet achieved the best overall performance on Dataset 1, with 99.20% accuracy, 99.40% recall, and 99.84% AUC. On the public EldeRan benchmark, it achieved 90.36% accuracy using the full dynamic feature space and 92.08% using a Mutual Information-selected compact subset. Paired Wilcoxon tests across five folds were used to assess the RL contribution, while additional grid-order sensitivity analysis showed that the image-based representation remained robust under five random 10 × 10 feature-grid permutations. Interpretability analysis using t-distributed stochastic neighbor embedding (t-SNE) and gradient-weighted class activation mapping feature-grid mapping further showed that the model captured discriminative behavioral patterns. Overall, these results demonstrate that RL-guided sample reweighting improves adaptive ransomware detection while maintaining efficiency and interpretability. The dataset and supporting code are publicly available on GitHub. Full article

(This article belongs to the Special Issue Intelligent Sensors for Security and Attack Detection)

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32 pages, 3089 KB

Open AccessArticle

Systematic Evaluation of Machine Learning and Deep Learning Models for IoT Malware Detection Across Ransomware, Rootkit, Spyware, Trojan, Botnet, Worm, Virus, and Keylogger

by Mazdak Maghanaki, Soraya Keramati, F. Frank Chen and Mohammad Shahin

Sensors 2026, 26(6), 1750; https://doi.org/10.3390/s26061750 - 10 Mar 2026

Cited by 8 | Viewed by 1839

Abstract

The rapid growth of Internet-of-Things (IoT) deployments has substantially expanded the attack surface of modern cyber–physical systems, making accurate and computationally feasible malware detection essential for enterprise and industrial environments. This study presents a large-scale, systematic comparison of 27 machine learning (ML) and 18 deep learning (DL) models for IoT malware detection across eight major malware categories: Trojan, Botnet, Ransomware, Rootkit, Worm, Spyware, Keylogger, and Virus. A realistic dataset was constructed using 50,000 executable samples collected from the Any.Run platform, including 8000 malware instances (1000 per class) and 42,000 benign samples. Each sample was executed in a sandbox to extract detailed static and behavioral telemetry. A targeted feature-selection pipeline reduced the feature space to 47 diagnostic features spanning static properties, behavioral indicators, process/file/registry activity, debug signals, and network telemetry, yielding a compact representation suitable for malware detection in IoT settings. Experimental results demonstrate that ensemble tree-based ML models consistently dominate performance on the engineered tabular feature set as 7 of the top 10 models are ML, with CatBoost and LightGBM achieving near-ceiling accuracy and low false-positive rates. Per-malware analysis further shows that optimal model choice depends on malware behavior. CatBoost is best for Trojan/Spyware, LightGBM for Botnet, XGBoost for Worm, Extra Trees for Rootkit, and Random Forest for Keylogger, while DL models are competitive only for specific categories, with TabNet performing best for Ransomware and FT-Transformer for Virus. In addition, an end-to-end computational time analysis across all 45 models reveals a clear efficiency advantage for boosted tree ensembles relative to most DL architectures, supporting deployment feasibility on commodity CPU hardware. Overall, the study provides actionable guidance for designing adaptive IoT malware detection frameworks, recommending gradient-boosted ensemble ML models as the primary deployment choice, with selective DL models only when category-specific gains justify additional computational cost. Full article

(This article belongs to the Special Issue Intelligent Sensors for Security and Attack Detection)

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27 pages, 496 KB

Open AccessArticle

An Intelligent Sensing Framework for Early Ransomware Detection Using MHSA-LSTM Machine Learning

by Abdullah Alqahtani, Mordecai Opoku Ohemeng and Frederick T. Sheldon

Sensors 2026, 26(3), 952; https://doi.org/10.3390/s26030952 - 2 Feb 2026

Cited by 5 | Viewed by 1601

Abstract

Ransomware represents a critical and evolving cybersecurity threat that often evades traditional defenses during its early stages. We present a novel intelligent sensing framework (ISF) designed for proactive, early-stage ransomware detection, centered on a Multi-Head Self-Attention Long Short-Term Memory (MHSA-LSTM) sensor model. The core innovation of this sensor is its self-attention mechanism, which is augmented to autonomously prioritize the most discriminative behavioral features by incorporating a relevance coefficient derived from information gain (

μ

), thereby filtering out noise and overcoming data scarcity inherent in initial attack phases. The framework was validated using a comprehensive dataset derived from the dynamic analysis of 39,378 ransomware samples and 9732 benign applications. The MHSA-LSTM sensor achieved superior performance, recording a peak accuracy of

98.4 %

, a low False Positive Rate (FPR) of

0.089

, and an F1 score of

0.972

using an optimized 25-feature set. This performance consistently surpassed established sequence models, including CNN-LSTM and Stacked LSTM, confirming the significant potential of the ISF as a robust and scalable solution for enhancing defenses against modern, stealthy threats. Most significantly, integration of

μ

as a statistical anchor resulted in a 49% reduction in False Positive Rates (FPRs) compared to standard attention-based models. This addresses the main operational barrier to deploying deep learning sensors in live environments. Full article

(This article belongs to the Special Issue Intelligent Sensors for Security and Attack Detection)

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Review

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29 pages, 2443 KB

Open AccessReview

Agentic and LLM-Based Multimodal Anomaly Detection: Architectures, Challenges, and Prospects

by Mohammed Ayalew Belay, Amirshayan Haghipour, Adil Rasheed and Pierluigi Salvo Rossi

Sensors 2026, 26(8), 2330; https://doi.org/10.3390/s26082330 - 9 Apr 2026

Viewed by 2793

Abstract

Anomaly detection is crucial in maintaining the safety, reliability, and optimal performance of complex systems across diverse domains, such as industrial manufacturing, cybersecurity, and autonomous systems. While conventional methods typically handle single data modalities, recently, there has been an increase in the application of multimodal detection in dynamic real-world environments. This paper presents a comprehensive review of recent research at the intersection of agentic artificial intelligence and large language-based multimodal anomaly detection. We systematically analyze and categorize existing studies based on the agent architecture, reasoning capabilities, tool integration, and modality scope. The main contribution of this work is a novel taxonomy that unifies agentic and multimodal anomaly detection methods, alongside benchmark datasets, evaluation methods, key challenges, and mitigation strategies. Furthermore, we identify major open issues, including data alignment, scalability, reliability, explainability, and evaluation standardization. Finally, we outline future research directions, with a particular emphasis on trustworthy autonomous agents, efficient multimodal fusion, human-in-the-loop systems, and real-world deployment in safety-critical applications. Full article

(This article belongs to the Special Issue Intelligent Sensors for Security and Attack Detection)

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