arXiv subjects

Hajime Suzuki

Publications and source records attributed to Hajime Suzuki.

At least 19 recordsLinked to original sources

arXiv2026

Tropospheric Ducting Prediction based on GFS Model Data at Inyarrimanha Ilgari Bundara, the CSIRO Murchison Radio-astronomy Observatory

Tropospheric ducting transports terrestrial RFI over hundreds of kilometres into the ARQZWA at Inyarrimanha Ilgari Bundara, the CSIRO Murchison Radio-astronomy Observatory. We present a ducting prediction method based on vertical refractivity profiles derived from GFS data, validated against seven years of continuous spectrum monitoring between 88 MHz and 2.68 GHz. Per-transmitter basic transmission loss to the site is computed with Rec ITU-R P.452-16 above 100 MHz and Rec ITU-R P.1812-6 below it, over Copernicus GLO-90 terrain, driven at each timestep by the GFS-derived refractivity lapse along the path. Skill is scored as a stratified AUC, computed within month and three-hour strata. The median stratified AUC is 0.724 and 24 of 25 confidence intervals exclude chance. A time-percentage calibration derived from four pilot months transfers without retuning to the full record, and skill is flat through three days of forecast lead. A pooled GBM on the same physical features, augmented by a duct-corridor connectivity metric, reaches a mean test AUC of 0.806 over all 25 channels and 0.799 over the 16 on which the analytical predictor is itself skilled. In-situ LTE cell decoding confirms attribution: 86-97% of over-the-horizon cell detections fall within flagged event hours, the range spanning the four monitored LTE channels, and the decoded network identities match the licensing records. Ship AIS receptions at 162 MHz, each a self-located 12 W transmitter at a known sea position, provide VHF ground truth. The FM channels retain only weak skill, far below the ducting channels, and we attribute their events to near-threshold local sources after excluding ducting, sporadic-E and aircraft scatter. The method enables adaptive scheduling of observations away from frequencies affected by forecast ducting, and drives a live forecast service at the observatory. (Abstract abridged for arxiv)

astro-ph.IM↗

arXiv2026

MulCovFuzz: A Multi-Component Coverage-Guided Greybox Fuzzer for 5G Protocol Testing

As mobile networks transition to 5G infrastructure, ensuring robust security becomes more important due to the complex architecture and expanded attack surface. Traditional security testing approaches for 5G networks rely on black-box fuzzing techniques, which are limited by their inability to observe internal program state and coverage information. This paper presents MulCovFuzz, a novel coverage-guided greybox fuzzing tool for 5G network testing. Unlike existing tools that depend solely on system response, MulCovFuzz implements a multi-component coverage collection mechanism that dynamically monitors code coverage across different components of the 5G system architecture. Our approach introduces a novel testing paradigm that includes a scoring function combining coverage rewards with efficiency metrics to guide test case generation. We evaluate MulCovFuzz on open-source 5G implementation OpenAirInterface. Our experimental results demonstrate that MulCovFuzz significantly outperforms traditional fuzzing approaches, achieving a 5.85\% increase in branch coverage, 7.17\% increase in line coverage, and 16\% improvement in unique crash discovery during 24h fuzzing testing. MulCovFuzz uncovered three zero-day vulnerabilities, two of which were not identified by any other fuzzing technique. This work contributes to the advancement of security testing tools for next-generation mobile networks.

cs.CR↗

arXiv2026

APFuzz: Towards Automatic Greybox Protocol Fuzzing

Greybox protocol fuzzing is a random testing approach for stateful protocol implementations, where the input is protocol messages generated from mutations of seeds, and the search in the input space is driven by the feedback on coverage of both code and state. State model and message model are the core components of communication protocols, which also have significant impacts on protocol fuzzing. In this work, we propose APFuzz (Automatic greybox Protocol Fuzzer) with novel designs to increase the smartness of greybox protocol fuzzers from the perspectives of both the state model and the message model. On the one hand, APFuzz employs a two-stage process of static and dynamic analysis to automatically identify state variables, which are then used to infer an accurate state model during fuzzing. On the other hand, APFuzz introduces field-level mutation operations for binary protocols, leveraging message structure awareness enabled by Large Language Models. We conduct extensive experiments on a public protocol fuzzing benchmark, comparing APFuzz with the baseline fuzzer AFLNET as well as several state-of-the-art greybox protocol fuzzers.

cs.CR↗

arXiv2025

From Description to Detection: LLM based Extendable O-RAN Compliant Blind DoS Detection in 5G and Beyond

The quality and experience of mobile communication have significantly improved with the introduction of 5G, and these improvements are expected to continue beyond the 5G era. However, vulnerabilities in control-plane protocols, such as Radio Resource Control (RRC) and Non-Access Stratum (NAS), pose significant security threats, such as Blind Denial of Service (DoS) attacks. Despite the availability of existing anomaly detection methods that leverage rule-based systems or traditional machine learning methods, these methods have several limitations, including the need for extensive training data, predefined rules, and limited explainability. Addressing these challenges, we propose a novel anomaly detection framework that leverages the capabilities of Large Language Models (LLMs) in zero-shot mode with unordered data and short natural language attack descriptions within the Open Radio Access Network (O-RAN) architecture. We analyse robustness to prompt variation, demonstrate the practicality of automating the attack descriptions and show that detection quality relies on the semantic completeness of the description rather than its phrasing or length. We utilise an RRC/NAS dataset to evaluate the solution and provide an extensive comparison of open-source and proprietary LLM implementations to demonstrate superior performance in attack detection. We further validate the practicality of our framework within O-RAN's real-time constraints, illustrating its potential for detecting other Layer-3 attacks.

cs.CR↗

arXiv2025

Robust Anomaly Detection in O-RAN: Leveraging LLMs against Data Manipulation Attacks

The introduction of 5G and the Open Radio Access Network (O-RAN) architecture has enabled more flexible and intelligent network deployments. However, the increased complexity and openness of these architectures also introduce novel security challenges, such as data manipulation attacks on the semi-standardised Shared Data Layer (SDL) within the O-RAN platform through malicious xApps. In particular, malicious xApps can exploit this vulnerability by introducing subtle Unicode-wise alterations (hypoglyphs) into the data that are being used by traditional machine learning (ML)-based anomaly detection methods. These Unicode-wise manipulations can potentially bypass detection and cause failures in anomaly detection systems based on traditional ML, such as AutoEncoders, which are unable to process hypoglyphed data without crashing. We investigate the use of Large Language Models (LLMs) for anomaly detection within the O-RAN architecture to address this challenge. We demonstrate that LLM-based xApps maintain robust operational performance and are capable of processing manipulated messages without crashing. While initial detection accuracy requires further improvements, our results highlight the robustness of LLMs to adversarial attacks such as hypoglyphs in input data. There is potential to use their adaptability through prompt engineering to further improve the accuracy, although this requires further research. Additionally, we show that LLMs achieve low detection latency (under 0.07 seconds), making them suitable for Near-Real-Time (Near-RT) RIC deployments.

cs.CR↗

arXiv2025

Active Attack Resilience in 5G: A New Take on Authentication and Key Agreement

As 5G networks expand into critical infrastructure, secure and efficient user authentication is more important than ever. The 5G-AKA protocol, standardized by 3GPP in TS 33.501, is central to authentication in current 5G deployments. It provides mutual authentication, user privacy, and key secrecy. However, despite its adoption, 5G-AKA has known limitations in both security and performance. While it focuses on protecting privacy against passive attackers, recent studies show its vulnerabilities to active attacks. It also relies on a sequence number mechanism to prevent replay attacks, requiring perfect synchronization between the device and the core network. This stateful design adds complexity, causes desynchronization, and incurs extra communication overhead. More critically, 5G-AKA lacks Perfect Forward Secrecy (PFS), exposing past communications if long-term keys are compromised-an increasing concern amid sophisticated threats. This paper proposes an enhanced authentication protocol that builds on 5G-AKA's design while addressing its shortcomings. First, we introduce a stateless version that removes sequence number reliance, reducing complexity while staying compatible with existing SIM cards and infrastructure. We then extend this design to add PFS with minimal cryptographic overhead. Both protocols are rigorously analyzed using ProVerif, confirming their compliance with all major security requirements, including resistance to passive and active attacks, as well as those defined by 3GPP and academic studies. We also prototype both protocols and evaluate their performance against 5G-AKA and 5G-AKA' (USENIX'21). Our results show the proposed protocols offer stronger security with only minor computational overhead, making them practical, future-ready solutions for 5G and beyond.

cs.CR↗

arXiv2025

Self-Adaptive and Robust Federated Spectrum Sensing without Benign Majority for Cellular Networks

Advancements in wireless and mobile technologies, including 5G advanced and the envisioned 6G, are driving exponential growth in wireless devices. However, this rapid expansion exacerbates spectrum scarcity, posing a critical challenge. Dynamic spectrum allocation (DSA)--which relies on sensing and dynamically sharing spectrum--has emerged as an essential solution to address this issue. While machine learning (ML) models hold significant potential for improving spectrum sensing, their adoption in centralized ML-based DSA systems is limited by privacy concerns, bandwidth constraints, and regulatory challenges. To overcome these limitations, distributed ML-based approaches such as Federated Learning (FL) offer promising alternatives. This work addresses two key challenges in FL-based spectrum sensing (FLSS). First, the scarcity of labeled data for training FL models in practical spectrum sensing scenarios is tackled with a semi-supervised FL approach, combined with energy detection, enabling model training on unlabeled datasets. Second, we examine the security vulnerabilities of FLSS, focusing on the impact of data poisoning attacks. Our analysis highlights the shortcomings of existing majority-based defenses in countering such attacks. To address these vulnerabilities, we propose a novel defense mechanism inspired by vaccination, which effectively mitigates data poisoning attacks without relying on majority-based assumptions. Extensive experiments on both synthetic and real-world datasets validate our solutions, demonstrating that FLSS can achieve near-perfect accuracy on unlabeled datasets and maintain Byzantine robustness against both targeted and untargeted data poisoning attacks, even when a significant proportion of participants are malicious.

cs.LG↗

arXiv2025

TELSAFE: Security Gap Quantitative Risk Assessment Framework

Gaps between established security standards and their practical implementation have the potential to introduce vulnerabilities, possibly exposing them to security risks. To effectively address and mitigate these security and compliance challenges, security risk management strategies are essential. However, it must adhere to well-established strategies and industry standards to ensure consistency, reliability, and compatibility both within and across organizations. In this paper, we introduce a new hybrid risk assessment framework called TELSAFE, which employs probabilistic modeling for quantitative risk assessment and eliminates the influence of expert opinion bias. The framework encompasses both qualitative and quantitative assessment phases, facilitating effective risk management strategies tailored to the unique requirements of organizations. A specific use case utilizing Common Vulnerabilities and Exposures (CVE)-related data demonstrates the framework's applicability and implementation in real-world scenarios, such as in the telecommunications industry.

cs.CR↗

arXiv2025

Variational Quantum Machine Learning with Quantum Error Detection

Quantum machine learning (QML) is an emerging field that promises advantages such as faster training, improved reliability and superior feature extraction over classical counterparts. However, its implementation on quantum hardware is challenging due to the noise inherent in these systems, necessitating the use of quantum error correction (QEC) codes. Current QML research remains primarily theoretical, often assuming noise-free environments and offering little insight into the integration of QEC with QML implementations. To address this, we investigate the performance of a simple, parity-classifying Variational Quantum Classifier (VQC) implemented with the [[4,2,2]] error-detecting stabiliser code in a simulated noisy environment, marking the first study into the implementation of a QML algorithm with a QEC code. We invoke ancilla qubits to logically encode rotation gates, and classically simulate the logically-encoded VQC under two simple noise models representing gate noise and environmental noise. We demonstrate that the stabiliser code improves the training accuracy at convergence compared to noisy implementations without QEC. However, we find that the effectiveness and reliability of error detection is contingent upon keeping the ancilla qubit error rates below a specific threshold, due to the propagation of ancilla errors to the physical qubits. Our results provide an important insight: for QML implementations with QEC codes that both require ancilla qubits for logical rotations and cannot fully correct errors propagated between ancilla and physical qubits, the maximum achievable accuracy of the QML model is limited. This highlights the need for additional error correction or mitigation strategies to support the practical implementation of QML algorithms with QEC on quantum devices.

quant-ph↗

arXiv2025

Quantum Down Sampling Filter for Variational Auto-encoder

Variational autoencoders (VAEs) are fundamental for generative modeling and image reconstruction, yet their performance often struggles to maintain high fidelity in reconstructions. This study introduces a hybrid model, quantum variational autoencoder (Q-VAE), which integrates quantum encoding within the encoder while utilizing fully connected layers to extract meaningful representations. The decoder uses transposed convolution layers for up-sampling. The Q-VAE is evaluated against the classical VAE and the classical direct-passing VAE, which utilizes windowed pooling filters. Results on the MNIST and USPS datasets demonstrate that Q-VAE consistently outperforms classical approaches, achieving lower Fréchet inception distance scores, thereby indicating superior image fidelity and enhanced reconstruction quality. These findings highlight the potential of Q-VAE for high-quality synthetic data generation and improved image reconstruction in generative models.

cs.CV↗

arXiv2024

Application of Quantum Pre-Processing Filter for Binary Image Classification with Small Samples

Over the past few years, there has been significant interest in Quantum Machine Learning (QML) among researchers, as it has the potential to transform the field of machine learning. Several models that exploit the properties of quantum mechanics have been developed for practical applications. In this study, we investigated the application of our previously proposed quantum pre-processing filter (QPF) to binary image classification. We evaluated the QPF on four datasets: MNIST (handwritten digits), EMNIST (handwritten digits and alphabets), CIFAR-10 (photographic images) and GTSRB (real-life traffic sign images). Similar to our previous multi-class classification results, the application of QPF improved the binary image classification accuracy using neural network against MNIST, EMNIST, and CIFAR-10 from 98.9% to 99.2%, 97.8% to 98.3%, and 71.2% to 76.1%, respectively, but degraded it against GTSRB from 93.5% to 92.0%. We then applied QPF in cases using a smaller number of training and testing samples, i.e. 80 and 20 samples per class, respectively. In order to derive statistically stable results, we conducted the experiment with 100 trials choosing randomly different training and testing samples and averaging the results. The result showed that the application of QPF did not improve the image classification accuracy against MNIST and EMNIST but improved it against CIFAR-10 and GTSRB from 65.8% to 67.2% and 90.5% to 91.8%, respectively. Further research will be conducted as part of future work to investigate the potential of QPF to assess the scalability of the proposed approach to larger and complex datasets.

cs.CV↗

arXiv2024

From 5G to 6G: A Survey on Security, Privacy, and Standardization Pathways

The vision for 6G aims to enhance network capabilities with faster data rates, near-zero latency, and higher capacity, supporting more connected devices and seamless experiences within an intelligent digital ecosystem where artificial intelligence (AI) plays a crucial role in network management and data analysis. This advancement seeks to enable immersive mixed-reality experiences, holographic communications, and smart city infrastructures. However, the expansion of 6G raises critical security and privacy concerns, such as unauthorized access and data breaches. This is due to the increased integration of IoT devices, edge computing, and AI-driven analytics. This paper provides a comprehensive overview of 6G protocols, focusing on security and privacy, identifying risks, and presenting mitigation strategies. The survey examines current risk assessment frameworks and advocates for tailored 6G solutions. We further discuss industry visions, government projects, and standardization efforts to balance technological innovation with robust security and privacy measures.

cs.CR↗

arXiv2024

Exploiting and Securing ML Solutions in Near-RT RIC: A Perspective of an xApp

Open Radio Access Networks (O-RAN) are emerging as a disruptive technology, revolutionising traditional mobile network architecture and deployments in the current 5G and the upcoming 6G era. Disaggregation of network architecture, inherent support for AI/ML workflows, cloud-native principles, scalability, and interoperability make O-RAN attractive to network providers for beyond-5G and 6G deployments. Notably, the ability to deploy custom applications, including Machine Learning (ML) solutions as xApps or rApps on the RAN Intelligent Controllers (RICs), has immense potential for network function and resource optimisation. However, the openness, nascent standards, and distributed architecture of O-RAN and RICs introduce numerous vulnerabilities exploitable through multiple attack vectors, which have not yet been fully explored. To address this gap and ensure robust systems before large-scale deployments, this work analyses the security of ML-based applications deployed on the RIC platform. We focus on potential attacks, defence mechanisms, and pave the way for future research towards a more robust RIC platform.

cs.CR↗

arXiv2024

Security and Privacy of 6G Federated Learning-enabled Dynamic Spectrum Sharing

Spectrum sharing is increasingly vital in 6G wireless communication, facilitating dynamic access to unused spectrum holes. Recently, there has been a significant shift towards employing machine learning (ML) techniques for sensing spectrum holes. In this context, federated learning (FL)-enabled spectrum sensing technology has garnered wide attention, allowing for the construction of an aggregated ML model without disclosing the private spectrum sensing information of wireless user devices. However, the integrity of collaborative training and the privacy of spectrum information from local users have remained largely unexplored. This article first examines the latest developments in FL-enabled spectrum sharing for prospective 6G scenarios. It then identifies practical attack vectors in 6G to illustrate potential AI-powered security and privacy threats in these contexts. Finally, the study outlines future directions, including practical defense challenges and guidelines.

cs.CR↗

arXiv2024

Systematic Literature Review of AI-enabled Spectrum Management in 6G and Future Networks

Artificial Intelligence (AI) has advanced significantly in various domains like healthcare, finance, and cybersecurity, with successes such as DeepMind's medical imaging and Tesla's autonomous vehicles. As telecommunications transition from 5G to 6G, integrating AI is crucial for complex demands like data processing, network optimization, and security. Despite ongoing research, there's a gap in consolidating AI-enabled Spectrum Management (AISM) advancements. Traditional spectrum management methods are inadequate for 6G due to its dynamic and complex demands, making AI essential for spectrum optimization, security, and network efficiency. This study aims to address this gap by: (i) Conducting a systematic review of AISM methodologies, focusing on learning models, data handling techniques, and performance metrics. (ii) Examining security and privacy concerns related to AI and traditional network threats within AISM contexts. Using the Systematic Literature Review (SLR) methodology, we meticulously analyzed 110 primary studies to: (a) Identify AI's utility in spectrum management. (b) Develop a taxonomy of AI approaches. (c) Classify datasets and performance metrics used. (d) Detail security and privacy threats and countermeasures. Our findings reveal challenges such as under-explored AI usage in critical AISM systems, computational resource demands, transparency issues, the need for real-world datasets, imbalances in security and privacy research, and the absence of testbeds, benchmarks, and security analysis tools. Addressing these challenges is vital for maximizing AI's potential in advancing 6G technology.

cs.NI↗

arXiv2024

Mitigation of Channel Tampering Attacks in Continuous-Variable Quantum Key Distribution

Despite significant advancements in continuous-variable quantum key distribution (CV-QKD), practical CV-QKD systems can be compromised by various attacks. Consequently, identifying new attack vectors and countermeasures for CV-QKD implementations is important for the continued robustness of CV-QKD. In particular, as CV-QKD relies on a public quantum channel, vulnerability to communication disruption persists from potential adversaries employing Denial-of-Service (DoS) attacks. Inspired by DoS attacks, this paper introduces a novel threat in CV-QKD called the Channel Amplification (CA) attack, wherein Eve manipulates the communication channel through amplification. We specifically model this attack in a CV-QKD optical fiber setup. To counter this threat, we propose a detection and mitigation strategy. Detection involves a machine learning (ML) model based on a decision tree classifier, classifying various channel tampering attacks, including CA and DoS attacks. For mitigation, Bob, post-selects quadrature data by classifying the attack type and frequency. Our ML model exhibits high accuracy in distinguishing and categorizing these attacks. The CA attack's impact on the secret key rate (SKR) is explored concerning Eve's location and the relative intensity noise of the local oscillator (LO). The proposed mitigation strategy improves the attacked SKR for CA attacks and, in some cases, for hybrid CA-DoS attacks. Our study marks a novel application of both ML classification and post-selection in this context. These findings are important for enhancing the robustness of CV-QKD systems against emerging threats on the channel.

quant-ph↗

arXiv2023

Radio Signal Classification by Adversarially Robust Quantum Machine Learning

Radio signal classification plays a pivotal role in identifying the modulation scheme used in received radio signals, which is essential for demodulation and proper interpretation of the transmitted information. Researchers have underscored the high susceptibility of ML algorithms for radio signal classification to adversarial attacks. Such vulnerability could result in severe consequences, including misinterpretation of critical messages, interception of classified information, or disruption of communication channels. Recent advancements in quantum computing have revolutionized theories and implementations of computation, bringing the unprecedented development of Quantum Machine Learning (QML). It is shown that quantum variational classifiers (QVCs) provide notably enhanced robustness against classical adversarial attacks in image classification. However, no research has yet explored whether QML can similarly mitigate adversarial threats in the context of radio signal classification. This work applies QVCs to radio signal classification and studies their robustness to various adversarial attacks. We also propose the novel application of the approximate amplitude encoding (AAE) technique to encode radio signal data efficiently. Our extensive simulation results present that attacks generated on QVCs transfer well to CNN models, indicating that these adversarial examples can fool neural networks that they are not explicitly designed to attack. However, the converse is not true. QVCs primarily resist the attacks generated on CNNs. Overall, with comprehensive simulations, our results shed new light on the growing field of QML by bridging knowledge gaps in QAML in radio signal classification and uncovering the advantages of applying QML methods in practical applications.

quant-ph↗

arXiv2023

Quantum-Inspired Machine Learning: a Survey

Quantum-inspired Machine Learning (QiML) is a burgeoning field, receiving global attention from researchers for its potential to leverage principles of quantum mechanics within classical computational frameworks. However, current review literature often presents a superficial exploration of QiML, focusing instead on the broader Quantum Machine Learning (QML) field. In response to this gap, this survey provides an integrated and comprehensive examination of QiML, exploring QiML's diverse research domains including tensor network simulations, dequantized algorithms, and others, showcasing recent advancements, practical applications, and illuminating potential future research avenues. Further, a concrete definition of QiML is established by analyzing various prior interpretations of the term and their inherent ambiguities. As QiML continues to evolve, we anticipate a wealth of future developments drawing from quantum mechanics, quantum computing, and classical machine learning, enriching the field further. This survey serves as a guide for researchers and practitioners alike, providing a holistic understanding of QiML's current landscape and future directions.

cs.LG↗