Mireille Elhajj

This paper proposes a novel interference detection method for Global Navigation Satellite System (GNSS) receivers based on a kernel-designed reduced interference distribution (RID) and a combined time–frequency (TF) representation. The proposed TF distribution is built via the Hadamard product of the Wigner–Ville distribution (WVD) and the RID (HP-WVD-RID), enabling effective suppression of cross-terms while preserving strong TF energy concentration. Compared with existed transformed-domain analysis techniques, the proposed method provides improved detection performance under severe interference conditions. Experimental results using interfered GPS-L1 C/A signals demonstrate a 47.19%improvement in interference frequency estimation accuracy over the smoothed pseudo Wigner–Ville distribution (SPWVD), and a 91.94% increase in zero transformed-domain energy percentage (ZTEP) compared with the fractional Fourier transform (FrFT), confirming its effectiveness for GNSS interference detection under challenging conditions.

Background: Health care systems face escalating cyberattacks, including the UK Synnovis ransomware attack, which halted pathology services for 14 weeks; the Ascension Health breach affecting 5.6 million patients; and the Change Healthcare breach costing US $2.5 billion. Conventional cybersecurity measures in health care remain reactive and inadequate against evolving threats. Machine learning (ML) offers adaptive, predictive, real-time cyber defense; yet, there is limited clarity on how ML tools are applied across cybersecurity domains, their real-world effectiveness, and where gaps remain. Objective: This study aims to map ML applications in health care cybersecurity against the National Institute of Standards and Technology Cybersecurity Framework version 2.0, summarize ML performance, and identify research gaps and implementation considerations. Methods: A systematic search of Ovid MEDLINE, Embase, and Scopus was conducted on July 30, 2025, for studies between 2019 and 2025. Eligible studies applied ML-based approaches to organizational-level cybersecurity in health care settings, with outcomes related to data privacy or cybersecurity strengthening. Studies on smart devices, blockchain, or those lacking empirical data were excluded. Title and abstract and full-text screening were conducted independently by 2 (KR and SZ) reviewers following the Arksey and O’Malley framework and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, with discrepancies resolved by consensus. Data were synthesized narratively and mapped against the 6 National Institute of Standards and Technology Cybersecurity Framework version 2.0 functions (Identify, Protect, Detect, Respond, Recover, and Govern). Results: From 10,348 articles identified, 45 studies across 18 countries were included, applying 80 ML models. Most studies addressed “Protect” (n=22, 48.9%), encompassing federated learning, homomorphic encryption, and deidentification pipelines. “Detect” (n=13, 28.9%) covered intrusion detection and anomaly-based threat detection. “Identify” (n=8, 17.8%) addressed risk assessment and vulnerability prediction. Only 2 studies addressed “Respond,” and none addressed “Recover” or “Govern.” Classical ML, deep learning, and natural language processing predominated, with intrusion detection being the most common application (n=29). Despite strong controlled performance, only one study demonstrated real-world deployment; most rely on synthetic or outdated benchmark datasets and inconsistent reporting metrics. Conclusions: To our knowledge, this is the first scoping review to map ML-driven cybersecurity solutions against all six National Institute of Standards and Technology Cybersecurity Framework version 2.0 functions, offering a structured, policy-relevant evidence base. ML applications remain concentrated in preincident functions, with gaps in response, recovery, and governance. This highlights systematic blind spots and priorities for researchers and implementers. The evidence supports a phased implementation approach: beginning with detection systems, where evidence is most established and integration is most feasible, progressing to privacy-preserving architectures, for which the literature currently offers no guidance. Implementation requires sustained investment in infrastructure, representative datasets, explainable AI, and real-world validation. Limitations include language bias, methodological heterogeneity, and exclusion of medical devices and proprietary solutions.

BACKGROUND Current cybersecurity practices in healthcare rely on conventional measures such as firewalls, antivirus software and access controls. These approaches are inadequate against sophisticated threats. Legacy systems, limited investment, and fragmented governance leave healthcare organisations vulnerable, highlighting the need for adaptive, predictive, and real-time solutions. Although Machine Learning (ML) methods exist for strengthening healthcare cyber-resilience, there is limited clarity on how these tools are applied across different cybersecurity domains, their relevance in real-world settings, and where critical gaps remain. OBJECTIVE This scoping review aimed to map and categorise current ML applications in healthcare cybersecurity against the National Institute of Standards and Technology Cybersecurity Framework, version 2.0 (NIST CSF), evaluate the effectiveness of existing approaches, and identify critical gaps and implementation priorities for healthcare organisations. METHODS A systematic search of Ovid MEDLINE, Embase, and Scopus was conducted on peer-reviewed studies published between 2019 to 2025. Search terms included “cybersecurity”, “healthcare”, “machine learning” and “artificial intelligence”. Inclusion criteria encompassed peer-reviewed studies applying PICOS criteria to organisational-level cybersecurity interventions in healthcare systems (hospitals, clinics, health networks) with outcomes related to data privacy, healthcare data protection, and cybersecurity practice strengthening. Study selection followed the Arksey and O’Malley framework and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews), independently conducted by two reviewers. Following data extraction, synthesis and thematic domain mapping based on categorisation according to the NIST CSF. RESULTS Across 45 studies 80 ML models were applied to healthcare systems cybersecurity, reflecting the broad scale and diversity of ML applications in healthcare cybersecurity. Most studies aligned with the ‘Protect’, ‘Identify’, and ‘Detect’ functions of the NIST CSF, showing a strong emphasis on security techniques for privacy-preserving, intrusion detection and risk assessment. ML models based on classic machine learning, deep learning and NLP were preferred. Only one study demonstrated real-world implementation, with the remainder limited to proof-of-concept stages. Sustained research and investment are needed to validate ML in real-world healthcare environments. These were limited by reliance on synthetic datasets and methodological heterogeneity. Barriers to real-world implementation include regulatory and infrastructural challenges. Future research efforts should focus on the development of explainable AI and representative datasets, alongside governance-focused applications. CONCLUSIONS Current ML models in healthcare cybersecurity remain heavily skewed toward prevention, detection, and identification, with a notable paucity of research addressing response and governance functions, as categorised by the NIST CSF. Healthcare organisations should adopt a phased operational implementation approach beginning with high-accuracy detection systems (95-99% accuracy), progressing to privacy-preserving techniques, and ultimately developing governance frameworks. This requires sustained budgeting for infrastructure, training, and validation costs to ensure safe, reproducible deployment of ML cybersecurity solutions.

This paper has proposed a new and efficient Global Navigation Satellite System (GNSS) anti-interference method using fractional Fourier Transform (FrFT) and notch filtering. An effective optimal fractional order determination model based on generalized time fractional bandwidth product (GTFrBP) of the FrFT has been developed in the detection of GNSS interfering signal, which can be realized using efficient one-dimensional search scheme and provide estimated interfering parameters. The GNSS interference mitigation can be realized through dechirping and notch filtering operations based on the obtained GNSS interference detection and estimation results. These proposed techniques in interference detection and mitigation are verified using real GPS L1-C/A signal interfered by the sweep interference. The results demonstrate that the developed GTFrBP search model delivers satisfactory precision in the determination of the optimal fractional order of the FrFT in the GNSS interference detection procedure; and the GNSS anti-interference experimental results have proven that the developed GNSS anti-interference method can successfully suppress the sweep interference present in the received GPS L1-C/A signal, which ensures that the acquisition performance of the GNSS receiver can be greatly improved in the interfering environments. The validity and effectiveness of the developed anti-interference method for GNSS receivers can be further verified by the carrier power-to-noise power density ratio (C/N0) curve and jamming tolerance analysis results, which have proven that the proposed FrFT and notch filtering based method is capable of providing significantly improved GNSS anti-interference performance in the interfering environments. Furthermore, the proposed GNSS anti-interference method has been tested in positioning experiments, which provides very satisfactory positioning results under interfering conditions, confirming the proposed method’s reliability and effectiveness in handling GNSS interference.

Interference is a major threat to the safe and secure operation of global navigation satellite system (GNSS) receivers particular in disturbing scenarios. Therefore, correct detection and reliable mitigation of interfering signals is critical to the delivery of mission critical positioning, navigation and timing services. In this paper, an effective interference detection and mitigation (ID&M) method based on the fractional Fourier transform (FrFT) has been proposed for GNSS receivers. This paper addresses the problem of inefficient search methods and unreliable interference detection and mitigation of the current techniques. Firstly, it develops an efficient optimal fractional order determination model First Order Moment of Magnitude (FOMM) of the FrFT to reduce the computational complexity compared with the traditional FrFT's maximum magnitude (MM) and generalized time fractional bandwidth product (GTFrBP) approaches. Secondly, an effective GNSS interference excision method using FrFT filtering has been developed to suppress GNSS interference. These proposed methods are tested in a controlled test bench experiment, for detecting and mitigating sweep interference, a major type of GNSS interfering signal generated by a GNSS jammer. The jamming tolerance analysis results show that the proposed FrFT filtering method is more than 25 dB higher than the state-of-the-art two-pole notch filter technique; the reliability of the proposed FrFT method has been further consolidated using effective carrier power-to-noise power density ratio analysis; and therefore, the proposed anti-interference method using FrFT filtering is recommended for use in GNSS receivers

A self-designed AMR detector named Smart Road Stud (SRS) is proposed. SRS, installed along lane markings, is not only a detector but also a lane markings enhancement device. Because a single SRS needs to detect vehicles on two lanes, the vehicle detection method for SRS is different from the AMR detectors installed in the middle of the lane or at the roadside. Based on SRS, an innovative mathematical model based on the magnetic dipole is developed to simulate vehicle magnetic field. According to the model, lane information can be inferred to achieve traffic volume detection. Results show that a single SRS delivers traffic volume detection accuracy of 97%, providing a strong foundation for future research to achieve higher detection performance based on multi-SRS linkage. The significance of this paper is to meet the detection requirements of warning equipment installed on lane markings.