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Intelligent Sensors for Security and Attack Detection
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Intelligent Sensors for Security and Attack Detection

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

Deadline for manuscript submissions: 15 July 2026 | Viewed by 3481

Special Issue Editors

Prof. Dr. Frederick Sheldon

E-Mail Website
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

E-Mail Website
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

E-Mail Website
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-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

  • 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 (3 papers)

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Research

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32 pages, 3089 KB  
Article
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
Viewed by 1068
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 [...] Read more.
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  
Article
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 3 | Viewed by 865
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 [...] Read more.
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  
Review
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 1079
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 [...] Read more.
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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