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20 pages, 1141 KB

Open AccessArticle

1D Convolution-Enhanced Mamba: A Method for Accurate Capture of Long-Sequence Stealthy DDoS Attacks

by Yi Li, Xingzhou Deng, Ang Yang and Jing Gao

Electronics 2026, 15(10), 2096; https://doi.org/10.3390/electronics15102096 - 14 May 2026

Viewed by 232

Abstract

Network technology has advanced rapidly in recent years, and distributed denial-of-service (DDoS) attacks have grown more diverse, stealthy, and large-scale. Traditional detection approaches struggle to process long network traffic sequences and locate sparse attack signals hidden in massive normal traffic, which makes accurate [...] Read more.

Network technology has advanced rapidly in recent years, and distributed denial-of-service (DDoS) attacks have grown more diverse, stealthy, and large-scale. Traditional detection approaches struggle to process long network traffic sequences and locate sparse attack signals hidden in massive normal traffic, which makes accurate and efficient DDoS detection an urgent requirement. This paper presents an end-to-end DDoS detection model built on the Mamba architecture. We use one-dimensional convolutions to extract local features and smooth noise, which strengthens the model’s ability to capture bursty attack behaviors. Then, taking advantage of Mamba’s linear complexity and selective scanning mechanism, the model models long traffic sequences, filters out redundant information, and concentrates on potential attack patterns. With global feature aggregation and a classification layer, the model realizes accurate attack recognition. Experiments conducted on the CIC-DDoS2019 dataset show that our model obtains better performance in weighted F1 score, precision, and recall, while also improving inference efficiency. The model is suitable for high-precision, low-latency DDoS detection in real network environments. Full article

(This article belongs to the Special Issue New Technologies for Cybersecurity)

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38 pages, 5627 KB

Open AccessArticle

Documenting Security Assurance for Resource-Constrained IIoT Devices—The CERTIFY Project Methodology

by Valerio Senni, Simone Fulvio Rollini and Fabio Federici

Electronics 2026, 15(6), 1337; https://doi.org/10.3390/electronics15061337 - 23 Mar 2026

Viewed by 380

Abstract

Securing resource-constrained Industrial Internet of Things (IIoT) devices requires guarantees of integrity and confidentiality. This paper presents a security framework developed under the CERTIFY project to document and justify trustworthy execution of sensitive software running on resource-constrained devices. Our architecture model assumes the [...] Read more.

Securing resource-constrained Industrial Internet of Things (IIoT) devices requires guarantees of integrity and confidentiality. This paper presents a security framework developed under the CERTIFY project to document and justify trustworthy execution of sensitive software running on resource-constrained devices. Our architecture model assumes the presence of a separation kernel, that employs hardware Memory Protection Units to enforce Memory isolation and mediate communications during boot, provisioning, and run-time. On the other side, the paper does not cover implementation aspects, but leaves them to the choice of the user intending to adopt the proposed solution architecture. To systematically show the trustworthiness of this proposed architecture, we develop an assurance case using Goal Structuring Notation: we map system requirements to architecture-specific security objectives and introduce a novel “attacker action” graphical element to explicitly integrate threat modeling into our arguments. The resulting assurance case provides a structured, auditable, and reusable foundation architecture for the secure implementation of the CERTIFY IIoT framework. Full article

(This article belongs to the Special Issue New Technologies for Cybersecurity)

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38 pages, 3950 KB

Open AccessEditor’s ChoiceArticle

Investigating Post-Quantum Cryptography to Secure Transmitted Data via Mobile Communication

by Rongjie Zhou, Huaqun Guo and Francis Ee Cheok Teo

Electronics 2026, 15(6), 1275; https://doi.org/10.3390/electronics15061275 - 18 Mar 2026

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Abstract

The advent of quantum computing poses significant challenges to traditional cryptographic systems, threatening the confidentiality, integrity and authenticity of digital communications. This paper investigates the integration of post-quantum cryptography (PQC) algorithms into mobile communication systems to address these challenges. The study focuses on [...] Read more.

The advent of quantum computing poses significant challenges to traditional cryptographic systems, threatening the confidentiality, integrity and authenticity of digital communications. This paper investigates the integration of post-quantum cryptography (PQC) algorithms into mobile communication systems to address these challenges. The study focuses on evaluating key PQC algorithms shortlisted by the National Institute of Standards and Technology (NIST), including CRYSTALS-Kyber, CRYSTALS-Dilithium, Falcon and SPHINCS+, within the context of 5G and future mobile network architectures. The research encompasses the design and implementation of an experimental framework involving mobile devices, servers, and cloud-based infrastructure to simulate real-world communication scenarios. Performance metrics such as key generation time, signature generation, encryption and decryption speed, and resource consumption were analyzed across various devices to identify algorithms suitable for mobile environments. The findings reveal that lattice-based algorithms, such as Kyber and Dilithium, offer a promising balance between security and efficiency, making them ideal for resource-constrained devices. In contrast, hash-based algorithms like SPHINCS+ exhibit higher computational demands, limiting their practicality in certain applications. This work highlights the importance of algorithm selection and hardware optimization in ensuring secure and efficient communications in the quantum era. By integrating theoretical advancements in PQC with practical applications, this research lays the foundation for quantum-resistant security in mobile networks, ensuring secure and future-ready digital communications. Full article

(This article belongs to the Special Issue New Technologies for Cybersecurity)

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20 pages, 4393 KB

Open AccessArticle

Enhancing the Detection of Cyber-Attacks to EV Charging Infrastructures Through AI Technologies

by Roberta Terruggia, Alberto Maldarella, Giovanna Dondossola and Gabriele Webber

Electronics 2025, 14(21), 4321; https://doi.org/10.3390/electronics14214321 - 4 Nov 2025

Cited by 3 | Viewed by 1946

Abstract

The increasing digitalization of energy infrastructures, particularly electric vehicle (EV) charging systems, has expanded their vulnerability to cyber threats. This paper presents a modular AI-driven platform for detecting attacks on EV charging infrastructures. The platform combines real-time data collection tools (Tshark, Nozomi Guardian, [...] Read more.

The increasing digitalization of energy infrastructures, particularly electric vehicle (EV) charging systems, has expanded their vulnerability to cyber threats. This paper presents a modular AI-driven platform for detecting attacks on EV charging infrastructures. The platform combines real-time data collection tools (Tshark, Nozomi Guardian, SNMP/Syslog, Elasticsearch Logstash and Kibana (ELK)) with Long Short-Term Memory (LSTM) Autoencoder-based anomaly detection. Data were gathered from a charging facility at RSE with twelve OCPP-J v1.6 charging stations, including normal and simulated Denial of Service (DoS) scenarios. The model, trained on multivariate time-series traffic data, achieved 97.1% Accuracy and 98.6% Recall, ensuring robust anomaly detection and minimizing false negatives. Although Precision was lower (52%) due to traffic variability, the system effectively detected both cyber-induced and operational anomalies, such as station disconnections. This work demonstrates the value of integrating deep learning with real-time monitoring to enhance the resilience of smart energy systems. Future developments will focus on improving precision, expanding protocol coverage, and addressing advanced threats such as data injection and Man-in-the-Middle attacks. Full article

(This article belongs to the Special Issue New Technologies for Cybersecurity)

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18 pages, 2103 KB

Open AccessArticle

Towards a Unified Quantum Risk Assessment

by Šarūnas Grigaliūnas and Rasa Brūzgienė

Electronics 2025, 14(17), 3338; https://doi.org/10.3390/electronics14173338 - 22 Aug 2025

Cited by 2 | Viewed by 2792

Abstract

Quantum computing poses an unprecedented threat to classical cryptography, requiring new risk assessment paradigms. This paper proposes a Quantum-Adjusted Risk Score (QARS) model, a theoretical and methodological innovation within the EU’s PAREK framework (Post-quantum asset and algorithm inventory, risk assessment, road mapping, execution, [...] Read more.

Quantum computing poses an unprecedented threat to classical cryptography, requiring new risk assessment paradigms. This paper proposes a Quantum-Adjusted Risk Score (QARS) model, a theoretical and methodological innovation within the EU’s PAREK framework (Post-quantum asset and algorithm inventory, risk assessment, road mapping, execution, key governance). QARS extends Mosca’s inequality—which defines a quantum threat timeline threshold—into a multi-factor risk scoring formula. We formalise QARS with mathematical expressions incorporating timeline, sensitivity, and exposure dimensions, each calibrated by factor weights and scaling functions. The design motivations for including these dimensions are discussed in depth. We present method for model calibration (including sector-specific weight adjustments) and outline validation strategies combining quantitative analysis and expert judgement. The proposed QARS model is situated in the context of the EU’s coordinated roadmap for post-quantum cryptography and cybersecurity regulations, illustrating how QARS supports compliance and strategic migration prioritisation. A prototype tool implementing QARS model is also provided to demonstrate practical applicability. Our contributions provide a unified approach to quantum risk assessment, marrying theoretical rigour with policy-relevant risk management needs to help organizations proactively address the quantum threat. Full article

(This article belongs to the Special Issue New Technologies for Cybersecurity)

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18 pages, 3949 KB

Open AccessArticle

Frequency-Domain Steganography with Hexagonal Tessellation for Vision–Linguistic Knowledge Encapsulation

by Hengxiao Chi, Ching-Chun Chang, Chin-Chen Chang and Jui-Chuan Liu

Electronics 2025, 14(7), 1379; https://doi.org/10.3390/electronics14071379 - 29 Mar 2025

Viewed by 1157

Abstract

With the rapid development of technologies such as vision–language modeling, sharing images with corresponding descriptions has become a common means of information transfer. Studying data-hiding techniques for JPEG images can protect sensitive descriptions, such as personal information associated with them while sharing images. [...] Read more.

With the rapid development of technologies such as vision–language modeling, sharing images with corresponding descriptions has become a common means of information transfer. Studying data-hiding techniques for JPEG images can protect sensitive descriptions, such as personal information associated with them while sharing images. Therefore, research on data-hiding techniques for JPEG images is of significant importance. However, existing methods that modify discrete cosine transform (DCT) coefficients still have room for improvement in increasing their embedding capacity while minimizing file size expansion. To address this issue, this paper proposes a knowledge encapsulation method for JPEG images using a special hexagonal tessellation matrix. First, a special hexagonal tessellation matrix is constructed based on the characteristics of non-zero AC coefficients. Then, non-zero AC coefficients in JPEG images are paired to form coordinate pairs, and the data are embedded by modifying the non-zero AC coefficient pairs into the coordinates corresponding to the secret data. Experimental results demonstrate that, compared to the previously proposed JPEG image data-hiding schemes, the proposed approach achieves a higher embedding capacity, a minimal file size increase (FSI), and an acceptable peak signal-to-noise ratio (PSNR). Full article

(This article belongs to the Special Issue New Technologies for Cybersecurity)

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Review

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41 pages, 3607 KB

Open AccessReview

The Hardware Isolation Gap: A Systematic Survey of BitLocker Forensics in the TPM 2.0 Era

by Vinay Kumar Sankalagere Ramesh, Sapna Vikram Mewundi, Prasad Balakrishna Honnavalli, Shashidhar Thoreshattalli Kenchaiah and Venkatesh Murthy Krishna Murthy

Electronics 2026, 15(9), 1959; https://doi.org/10.3390/electronics15091959 - 6 May 2026

Viewed by 1056

Abstract

BitLocker Drive Encryption has evolved from an optional feature into a hardware-integrated encryption framework widely deployed on modern Windows systems with TPM-based device encryption enabled by default. This survey is an in-depth analysis of the architecture and Trusted Platform Module (TPM) incorporation of [...] Read more.

BitLocker Drive Encryption has evolved from an optional feature into a hardware-integrated encryption framework widely deployed on modern Windows systems with TPM-based device encryption enabled by default. This survey is an in-depth analysis of the architecture and Trusted Platform Module (TPM) incorporation of BitLocker and 18-years of attack vector development (2006–2025). We record a progressive loss of forensic practicability: the reported attack success rates are now down to less than 5% in current TPM 2.0 defended systems as compared to the 60–70% success rates reported in early software based implementations. We name and define the Hardware Isolation Gap—a paradigm shift in architecture where encryption keys are implanted into hardware, and where the traditional offline and memory based forensic methods do not work anymore by design. To the best of our knowledge, this is the first version-correlated attack taxonomy, which shows that the practical attack surface size is reduced by approximately 80%. We also provide exploratory empirical validation using controlled experiments on a single Intel-based Windows 11 platform (Dell Precision 5570 (Xiamen, China), Intel Core i7-12800H with firmware TPM); while these results directionally confirm literature-reported forensic success rates below 5% against default TPM 2.0 settings, they represent preliminary single-platform observations and may not generalize to AMD Ryzen, Microsoft Pluton, or other OEM hardware configurations. We have found that forensic inaccessibility today is not the failure of tooling but rather the intentional result of security engineering. Literature suggests that cloud-based recovery and weak-password cracking remain the only viable strategies against default Windows 11 settings; however, cloud-based recovery was not empirically tested in this study due to ethical and legal constraints and is characterized based on secondary literature analysis. We determine the existence of crucial methodological gaps and suggest a failure-conscious forensic research agenda that can act within the hard cryptographic constraints. Full article

(This article belongs to the Special Issue New Technologies for Cybersecurity)

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23 pages, 515 KB

Open AccessReview

Cybersecurity of Unmanned Aerial Vehicles from a Control Systems Perspective: A Review

by Ben Graziano and Arman Sargolzaei

Electronics 2026, 15(1), 163; https://doi.org/10.3390/electronics15010163 - 29 Dec 2025

Cited by 1 | Viewed by 1372

Abstract

Unmanned aerial vehicles (UAVs) are widely utilized for environmental monitoring, precision agriculture, infrastructure inspection, and various defense missions, including reconnaissance and surveillance. Their cybersecurity is essential because any compromise of communication, navigation, or control systems can cause mission failure and introduce significant safety [...] Read more.

Unmanned aerial vehicles (UAVs) are widely utilized for environmental monitoring, precision agriculture, infrastructure inspection, and various defense missions, including reconnaissance and surveillance. Their cybersecurity is essential because any compromise of communication, navigation, or control systems can cause mission failure and introduce significant safety and security risks. Therefore, this paper examines the existing literature on UAV cybersecurity and highlights that most previous surveys focus on listing different types of attacks or communication weaknesses, rather than evaluating the problem from a control systems perspective. Considering control systems is important because the safety and performance of a UAV depend on how cyberattacks affect its sensing, decision-making, and actuation loops; modeling these attacks and their impact on system behavior provides a clearer foundation for designing secure, resilient, and stable control strategies. Based on a comprehensive review of the literature, it presents a mathematical framework for characterizing common cyberattacks on UAV communication and sensing layers, including time-delay switch, false data injection, denial of service, and replay attacks. To demonstrate the impacts of these attacks on UAV control systems, a simulation of a two-UAV leader-follower multi-agent system is conducted in MATLAB. Defense algorithms from the existing literature are then organized into a hierarchical framework of prevention, detection, and mitigation, with detection and mitigation further categorized into model-based, learning-based, and hybrid approaches that combine both. The paper concludes by summarizing key findings and highlighting challenges with current defense strategies, including those insufficiently addressed in existing research. Full article

(This article belongs to the Special Issue New Technologies for Cybersecurity)

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