論文 - 大槻　知明

Enhanced Few-Shot Malware Traffic Classification via Integrating Knowledge Transfer With Neural Architecture Search

Zhang X., Wang Q., Qin M., Wang Y., Ohtsuki T., Adebisi B., Sari H., Gui G.

IEEE Transactions on Information Forensics and Security 19   5245 - 5256 2024年

研究論文（学術雑誌）, 責任著者,  ISSN  15566013

　概要を見る

Malware traffic classification (MTC) is one of the important research topics in the field of cyber security. Existing MTC methods based on deep learning have been developed based on the assumption of enough high-quality samples and powerful computing resources. However, both are hard to obtain in real applications especially in availability of IoT. In this paper, we propose a few-shot MTC (FS-MTC) method combining knowledge transfer and neural architecture search (i.e. NAS-based FS-MTC) with limited training samples as well as acceptable computational resources, in order to mitigate the identified challenges. Specifically, our proposed method first converts the raw network traffic into traffic images through data pre-processing to serve as input data for the neural network. Second, we use neural architecture search to adaptively search for the effective feature extraction model on the source domain (including Edge-IIoTset, Bot-IoT, and benign USTC-TFC2016). Third, the searched model is pre-trained on source task to achieve the generic feature representation of malware traffic. Finally, we only use few-shot malware traffic samples to fine-tune the pre-trained model to quickly adapt to new types of MTC tasks in realistic network environments. The experimental results show that the proposed NAS-based FS-MTC method has great scalability and classification performance in different FS-MTC tasks, including 5-way K-shot USTC-TFC2016 dataset and 10-way K-shot CIC-IoT dataset. Compared with state-of-the-art methods in the field of malware classification, the proposed NAS-based FS-MTC has higher classification accuracy. Especially in the 1-shot case of the USTC-TFC2016 dataset, its average accuracy is as high as 86.91%.

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Fully Autonomous Distributed Transmission Parameter Selection Method for Mobile IoT Applications Using Deep Reinforcement Learning

Sugiyama S., Makizoe K., Arai M., Hasegawa M., Otsuki T., Li A.

IEEE Vehicular Technology Conference 2024年

ISSN  15502252

　概要を見る

With the rapid increase in Internet of Things (IoT) devices, packet collision has become a serious problem in Long Range (LoRa) communication. Furthermore, the rising demand for mobile IoT applications necessitates parameter allocation methods that take into account the mobility of End Devices (EDs). However, the existing transmission parameter selection methods considering mobility are almost centralized designs, which may increase the probability of interference and system latency. The decentralized design is few and requires prior information on ED, which may cause scalability issues, e.g., adding ED to an existing network. To solve the problems above, a method through a fully autonomous distributed design supposing the mobility of ED using Double Deep Q Network (DDQN) is proposed in this paper. In the proposed method, ED trains the model based on ACKnowledge (ACK) packets and its location information without any prior information. Performance evaluation results show that the proposed method can achieve a higher packet delivery rate than other autonomous distributed methods in mobile IoT scenarios.

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Enhanced User Clustering and Pairing Scheme for NOMA-Aided UAV Networks

Yin Y., Bouazizi M., Yang S., Ohtsuki T.

IEEE Vehicular Technology Conference 2024年

ISSN  15502252

　概要を見る

The increasing demand for spectral efficiency and system capacity in communication networks has driven the integration of Non-Orthogonal Multiple Access (NOMA) technology with Unmanned Aerial Vehicle (UAV) networks. In this paper, we propose an enhanced user clustering and pairing scheme for NOMA-aided UAV networks. Our study aims to maximize the minimum user throughput by proposing an Advanced Balanced K-Means (ABKM) algorithm, based on the traditional K-Means (KM) and Balanced K-Means (BKM) algorithms. The ABKM algorithm addresses the issue in the BKM algorithm where some users are assigned to sub-optimal clusters, resulting in increased distances to the cluster centroids, while simultaneously ensuring balanced clustering. Through extensive numerical simulations, we demonstrate that the ABKM algorithm significantly outperforms KM and BKM algorithms in terms of the minimum user throughput. The results highlight the potential of the proposed ABKM algorithm to enhance system performance and ensure fair resource allocation in NOMA-aided UAV networks, making it a promising solution for future 6G wireless communication systems.

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Enhanced Resource Allocation in Vehicular Networks via Multi-Agent Reinforcement Learning

Zhang Y., Wang S., Hua M., Zhang Y., Wang Y., Tomoaki O., Sari H., Gui G.

IEEE Vehicular Technology Conference 2024年

ISSN  15502252

　概要を見る

The rapid changes in high-mobility vehicle environments make it challenging for base stations (BS) to obtain comprehensive channel state information. Furthermore, road and traffic safety require communication with low latency and high reliability, posing significant challenges to spectrum resource allocation in vehicular networks. To address these challenges, this paper proposes a method combining dueling double deep-Q network (D3QN) reinforcement learning (RL) with long short term memory (LSTM) network. By using a Manhattan Grid Layout City Model as the foundational environment, a multi-agent model is constructed, with each vehicle-to-vehicle (V2V) link acting as an individual agent. These agents collaborate and interact with the environment, receiving feedback, and then determining the optimal resource allocation to ensure both superior mobile service and a safe driving environment. The experimental results indicate that our proposed method outperforms the conventional D3QN network in both the vehicle-to-infrastructure (V2I) links and the V2V links.

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ECGDiff: Conditional Diffusion Model for ECG Reconstruction from Doppler Signals

Feghoul K., Bouazizi M., Feghoul R., Ohtsuki T.

IEEE International Conference on Communications    232 - 237 2024年

ISSN  15503607

　概要を見る

Electrocardiogram (ECG) measurement is a fundamental diagnostic and monitoring tool in cardiology and healthcare. It plays a crucial role in identifying and managing cardiac conditions, assessing heart function, and improving patient outcomes through timely interventions and treatment adjustments. Conventional approaches for ECG measurement are often unsuitable for everyday use due to their invasiveness, inaccessibility, or limited accuracy. In this context, we introduce ECGDiff, a novel approach based on conditional denoising diffusion models for ECG signals reconstruction from Doppler signals. ECGDiff provides a non-intrusive and accessible method for non-contact ECG measurement. Our method represents a pioneering use of denoising diffusion models for time series trans-lation. In the proposed framework, ECG signals are iteratively reconstructed from random noise using preprocessed signals derived from a Doppler sensor as input conditions. Extensive experiments have been conducted on a dataset comprising 19 healthy individuals, consisting of pairs of synchronized Doppler and ECG signals. Our experimental results demonstrate that the proposed ECGDiff outperforms other state-of-the-art methods by a large margin for the task of ECG signals reconstruction. Specifically, we achieved a DTW score of 8.32, a Frechet distance score of 3.10, and a Pearson correlation coefficient of 0.93. These results highlight the effectiveness of our approach for reconstructing ECG signals from Doppler signals.

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DyFormer: A CNN-Transformer Network Combining Local and Global Attention for Optical Remote Sensing Object Detection

Huang Y., Wang Q., Jiao D., Wang S., Wang Y., Lu Z., Ohtsuki T., Gui G.

2024 4th International Conference on Artificial Intelligence Virtual Reality and Visualization Aivrv 2024    145 - 149 2024年

　概要を見る

Remote sensing image (RSI) object detection is crucial for natural disaster management, urban planning, and resource exploration. However, due to significant differences between RSIs and natural images (NIs), existing object detectors designed for NIs are not directly applicable to RSIs. CNN-based models typically require specialized attention modules to relate small targets to global contexts, while Transformer-based models often add modules to enhance detail extraction, increasing computational costs for edge deployment. To address these challenges, we propose DyFormer, a hybrid CNN-Transformer model that combines local and global attention mechanisms for effective feature fusion. Additionally, a feature residual pyramid network (FRPN) is introduced to enhance multi-scale feature integration. We conduct experiments on the open source NWPU VHR-10 dataset to evaluate the proposed DyFormer, achieving a mAP@0.5 of 61.7%, outperforms existing sort-of-the-art methods, demonstrating its effectiveness for RSI object detection tasks.

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DTL-Based CSI Feedback Combined with Continual Learning in FDD Massive MIMO Systems

Inoue M., Ohtsuki T., Bouazizi M.

2024 International Conference on Computing Networking and Communications Icnc 2024    1077 - 1081 2024年

　概要を見る

The Deep Transfer Learning (DTL)-based Channel State Information (CSI) approach exploits the Deep Neural Network (DNN) model to provide low-cost CSI feedback for the target channel. It involves offline training on a given CSI dataset, followed by fine-tuning for a new environment using a small amount of collected CSI data, reducing feedback costs. However, in practical scenarios, the fine-tuned model performs worse on the source channel compared to the original source model. This leads to continued use of the poorly performing target model until it's fine-tuned again with newly acquired data. To address this, we propose combining DTL-based CSI feedback with continual learning. We introduce elastic weight consolidation (EWC) into the loss function during fine tuning. Simulations show that our method significantly reduces the degradation of the target model on the source channel, as measured by NMSE, compared to a method without continual learning.

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Deep Sub-Image Sampling Based Defense Against Spatial-Domain Adversarial Steganography

Huang X., Cao Y., Ohtsuki T.

2024 IEEE International Conference on Acoustics Speech and Signal Processing Workshops Icasspw 2024 Proceedings    383 - 387 2024年

　概要を見る

Deep steganalyzer combined with neural networks has achieved great success in image classification over recent years. However, it suffers from the following persistent challenges: i) Deep steganalyzer is extremely vulnerable and has the risk of being attacked via adversarial steganography when performing the image classification tasks; ii) Pre-processing based methods aiming to remove adversarial perturbations from cover images jeopardize the accuracy performance, as the involved steganographic signal will be wiped off as well. In this context, to defend against adversarial attacks by an adversary, we propose an adversarial steganography detection scheme based on the pre-processing and feature migration. In brief, sub-images are sampled to obtain the dimensionality of the extracted features, which are usually used to expand them while reducing the effect brought by adversarial perturbations. In particular, by computing statistical features together with normalizing the features, our approach can improve the classification accuracy of the samples. Our experimental results show that the proposed approach is capable of detecting adversarial steganographic image with an accuracy gain of up to 35.9% over the state-of-the-art methods.

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Deep Learning-based Open-set Person Identification using Radar Extracted Cardiac Signals

Xing Z., Bouazizi M., Ohtsuki T.

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBS 2024年

ISSN  1557170X

　概要を見る

Person identification based on radar-extracted vital signs has become increasingly popular due to its non-contact measurement capabilities. This paper introduces a novel deep learning-based person identification algorithm leveraging radar- extracted vital signs. While current studies mainly focus on closeset conditions with consistent training and testing categories, real-world scenarios often involve open-set circumstances, in which there are more data categories in the testing data. The algorithm involves extracting heart pulse signals from Doppler radar echoes, training two Convolutional Neural Network (CNN)-based models using transfer learning, and utilizing a distribution model for calibration. By combining the models' outputs through a strategic decision-making process, we achieve superior person identification results. Experimental results on a public radar vital signs dataset demonstrate an identification accuracy of 99.61% in close-set conditions and 94.35% in openset conditions, surpassing existing approaches.

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Cramér-Rao Lower Bound and Fairness Optimization in STAR-RIS Assisted ISAC Systems

Wang R., Cao Y., Ohtsuki T., He J.

IEEE Vehicular Technology Conference 2024年

ISSN  15502252

　概要を見る

This paper studies user fairness of an integrated sensing and communication (ISAC) system adopting both simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and non-orthogonal multiple access (NO-MA). Note that optimizing user fairness of an ISAC system is critical, since the performance trade-off in terms of spectrum resource utilization between communication users and sensing users is hard to balance and improve, particularly at the low signal-to-noise ratio (SNR) regimes. However, such optimization problem is a coupled, non-convex, and is NP-hard in general. To solve this challenging problem, a low-complexity algorithm based on both the successive convex approximation and the semi-deterministic programming techniques is proposed. Notably, the proposed approach can maximize the signal-to-interference-plus-noise ratio (SINR) for the weakest communication user, while guaranteeing the superior sensing performance characterized by the cramér-rao lower bound (CRLB) for the sensing user. Simulation results demonstrate that our approach is capable of enabling superior sum-rate performance than the STAR-RIS with orthogonal multiple access (OMA), the conventional-RIS with NOMA, and the conventional-RIS with OMA schemes.

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Coverage and Rate Performance for Distributed Multi-RIS-Assisted mmWave Communications

Cao Y., Wu X., He J., Ohtsuki T.

IEEE Vehicular Technology Conference 2024年

ISSN  15502252

　概要を見る

Millimeter-wave (mmWave) communication, which operates at high frequencies, has gained extensive research interest due to its significantly wide spectrum and short wavelengths. However, mmWave communication suffers from the notable drawbacks as follows: i) The mmWave signals are sensitive to the blockage, which is caused by the weak diffraction ability of mmWave propagation. ii) The mmWave communication incorporating with reconfigurable intelligent surfaces (RISs) is efficient in overcoming the performance degradation caused by severe path loss. Nevertheless, the location of users and RISs as well as their densities impose a significant impact on the coverage and rate performance. Motivated by the challenges above and based on the stochastic geometry concept, we first construct distributed multi-RIS-aided mmWave communication system over Nakagami-m fading channel. Afterward, we analyze the end-to-end (E2E) signal-to-interference-plus-noise-ratio (SINR) coverage and rate performance of the system. To improve the system performance in term of the E2E SINR coverage probability and achievable rate, we study the optimization of the phase-shifting control of the distributed RISs and optimize the E2E SINR coverage particularly when installing a large number of reflecting elements in RISs. To facilitate the study, we optimize the dynamic association criterion between the source and destination. Our simulation results demonstrate that the deployment of distributed RISs can significantly improve the E2E SINR coverage probability and achievable rate of the system compared to the selected benchmarks.

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Chirp Correction and Phase Accumulation-Linear Interpolation-assisted ICEEMDAN-based Heart Rate Estimation from MIMO FMCW Radar

Mutsukawa M., Shi X., Ohtsuki T.

2024 IEEE International Conference on E Health Networking Application and Services Healthcom 2024 2024年

　概要を見る

Non-contact heart rate measurement is anticipated to be used in various scenarios such as medical and disaster sites, driver monitoring, and smart homes. Millimeter-wave radar-based non-contact heart rate measurement leverages the chest wall displacement signal, which is a superposition of heartbeat and respiration. However, the chest wall displacement signals are susceptible to higher harmonics of respiration and other intermodulation harmonics, which can impede heart rate measurement. In this paper, we investigate a heart rate estimation method utilizing MIMO (Multiple-Input Multiple-Output) FMCW (Frequency Modulated Continuous Wave) radar. Conventional methods that utilize mode decomposition are not robust enough, as the accuracy of the results is highly sensitive to the SNR (Signal-to-Noise Ratio) of the selected IMFs (Intrinsic Mode Functions). Therefore, we propose performing chirp correction in addition to the conventional method that combines PA-LI (Phase Accumulation-Linear Interpolation) and ICEEMDAN (Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) to improve the accuracy of heart rate estimation. Our experimental results show that this approach enhances the SNR of the decomposed signal containing heart rate information and improves the accuracy of heart rate estimation.

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Camera-based Stress Detection using Face-related and Emotion-related Features

Ogasawara R., Bouazizi M., Ohtsuki T.

2024 IEEE International Conference on E Health Networking Application and Services Healthcom 2024 2024年

　概要を見る

Mental stress is something we experience on a daily basis. However, when it becomes chronic, it has various negative effects on our body. Although stress monitoring methods based on physiological signals are effective, they may not provide a comfortable experience for users, as they require sensors to be worn or attached to the body. As such, the need for non-invasive, non-contact and comfortable methods arises. In this paper, we propose a stress detection method using face-related and emotion-related features, all of which can be acquired by using a camera. Face-related features include action units and face embedding. Emotion related features include valence, arousal and emotion-related labels. Through these features and their fusions we achieved accuracy of 1.000 for stress detection, 0.891 for task recognition and 0.883 for stress level classification, respectively, with the sample sizes of 111, 316, and 319, respectively. These results demonstrate that our proposed method using face-related and emotion-related features and their fusion is effective in stress monitoring and detection.

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Beamforming Design using UE Positions and 3D Terrain-Building in HAPS System

Li Z., Yang S., Yin Y., Bouazizi M., Ohtsuki T.

IEEE Vehicular Technology Conference 2024年

ISSN  15502252

　概要を見る

High Altitude Platform Stations (HAPS) are instrumental in wireless communications, providing enhanced connectivity and extensive coverage by complementing ground-based infrastructure where its expansion is limited. HAPS enhance wireless communications by employing advanced beamforming technology, traditionally based on 2D with free space path loss models. Addressing the inadequacy of these models in different areas, our research introduces a novel beamforming strategy that incorporates detailed 3D geographic and architectural information. This approach models line-of-sight (LOS) and non-line-of-sight (NLOS) conditions with 3D information and optimizes beamforming patterns using Deep Reinforcement Learning (DRL). Our results indicate that by incorporating 3D information, the beamforming performance in terms of average throughput and SINR is markedly enhanced across all user equipments (UEs), compared to traditional 2D approaches. By taking 3D information into account, beamforming in HAPS systems becomes more equitable.

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Age-Based Federated Learning Approach to In-Network Caching: An Online Scheduling Policy

Cao Y., Maghsudi S., Ohtsuki T.

IEEE International Conference on Communications    1437 - 1442 2024年

ISSN  15503607

　概要を見る

We develop an accurate real-time scheduling framework for federated learning (FL) in wireless caching networks to guarantee the successful delivery of files at a low cost and with a short delay. The following persisting challenges motivated our work: i) Enforcing an excessive number of FL model update per communication round is infeasible due to the limited backhaul spectrum; ii) Naive scheduling policy accounting for FL model update renders service backlogs, thus leading to network parameter staleness and in-network caching utility (ICU) deterioration. Optimal scheduling in FL is challenging, as the mobile users' preferences for content, request patterns, and network traffic are dynamic and unknown. To tackle that challenge, we first formulate an instantaneous ICU optimization problem against the stale FL models. Afterward, based on the concept of age-of-update (AoU), we propose a federated learning with an unsatisfactory set selection (FedUSS) approach capable of executing the multiple-tasks of short-term predictions and making cache replacement decisions at low cost. Theoretical and numerical analyses manifest the effectiveness of our approach.

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A Low-Complexity Clustering-Aided DQN Method for Dynamic Antenna Control in HAPS

Bouazizi M., Yang S., Ohtsuki T.

IEEE Vehicular Technology Conference 2024年

ISSN  15502252

　概要を見る

This paper aims to address the issue of low through-put for users, caused by the random movement of High-Altitude Platform Stations (HAPS) due to winds. The proposed solution involves developing an Equal Clustering (EC) approach that groups users into high-density clusters, ensuring an equal num-ber of users in each cluster while maintaining low complexity. To further enhance the system's throughput performance, we fine-tune the antenna parameters using a Deep Q-Network (DQN) and the results of the EC clustering. To evaluate the effectiveness of the proposed method, we compare it with three Reinforcement Learning (RL)-based approaches and a K-Means clustering-based method. Simulation results indicate that both the EC method and the EC-aided DQN method successfully enhance the Cumulative Distribution Function (CDF) performance of throughput distribution when compared to the RL-based method for both rotation and shift scenarios. Furthermore, the EC-aided DQN method outperforms the K-Means clustering-based method in terms of the CDF of throughput performance.

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A CSI-based Cross-subject Transfer Learning Method Using Network Freezing

He Z., Bouazizi M., Ohtsuki T.

Proceedings IEEE Global Communications Conference Globecom    3515 - 3520 2024年

ISSN  23340983

　概要を見る

Currently, the field of wireless sensing is moving towards non-contact and easy-to-deploy passive sensing technologies. Among these, sensing techniques based on WiFi channel state information (CSI) have emerged as highly promising due to their excellent performance and suitability for current sensing needs. However, despite the potential of WiFi CSI-based sensing technologies, there are still pressing issues that need to be addressed. Traditional WiFi CSI-based methods face challenges such as weak generalization ability and poor robustness, especially when the sensing target and environment change, which hinders the further development. To address this problem, this paper introduces the transfer learning method and combines it with the deep neural network to propose a novel cross-subject WiFi sensing method. Specifically, this method utilizes the source domain-target domain partitioning approach in transfer learning. In the source domain, deep neural networks are trained to obtain the source domain feature model, which is then transferred to the target domain. Fine-tuning is performed using techniques such as network freezing, aiming to meet the faster and more accurate sensing task demands of cross-subject. From the final experimental results, the novel cross-subject transfer learning method proposed in this paper achieved higher recognition accuracy and shorter training time. Moreover, the implementation of network freezing further enhanced the performance and efficiency of cross-subject, achieving up to 86% performance on the dataset used in this paper.

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A Comparative Study of Methods of Person Identification Using Radar-Extracted Heartbeat Signals

Liu K., Bouazizi M., Xing Z., Ohtsuki T.

IEEE Access 12   152196 - 152211 2024年

　概要を見る

In recent years, non-contact biometric identification has garnered significant attention owing to its flexibility and its capacity to ensure privacy and confidentiality. Previous research has predominantly centered on harnessing cardiac radar signals detected by radar systems. This paper aims to compare two methods for person identification utilizing heartbeat signals extracted from radar data. In the first approach, we directly employ the heartbeat time series data as input for a deep learning model called InceptionTime dedicated to person identification. The second method explores the viability of using spectrograms, which are images generated from cardiac radar heartbeat signals, combined with other deep learning models, ResNet and a small convolutional neural network (CNN) model built by us, to achieve the same goal. Additionally, we assessed the robustness of both methods by introducing white Gaussian noise into the signals and evaluating their performance under different noise levels. For the second method, we implemented three image denoising methods: autoencoder, deep image prior (DIP), and Dilated-Residual U-Net (DRU-Net) to denoise the spectrograms after adding noise in the time domain. Experimental results reveal that the accuracy of person identification using spectrograms is 100% without adding noise based on the public dataset. The identification accuracy is the same as that using time series data. Following the introduction of noise, the accuracy of both methods decreases; however, the method based on spectrograms exhibits a more pronounced decline in accuracy compared to the approach using time series data directly especially for the small CNN model we built. The image-based method with DIP image denoiser shows outstanding performance when the value of noise is high compared with InceptionTime.

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3D-LiDAR-Based Fall Detection by Dynamic Non-Linear Mapping with LSTM

Meng X., Bouazizi M., Ohtsuki T.

IEEE International Conference on Communications    818 - 823 2024年

ISSN  15503607

　概要を見る

With the aging of the population, the health of the elderly people has become a concern for many families. Monitoring of elderly people and the detection of their activities have become hot research topics. Several approaches have been proposed in the literature to detect such activity (e.g., falls), but their reliance on RGB cameras poses a serious threat to the privacy of the elderly. Therefore, Light Detection and Ranging (LiDAR) has attracted the attention of researchers. In this paper, we propose a novel method for fall detection using transfer learning and dynamic non-linear mapping to extract the human skeletons from depth images and implement a fall detection method using a Long Short-Term Memory (LSTM) neural network. We collected RGB images and depth images using 3D-LiDAR. The RGB images are used as a ground-truth for annotation and evaluation, whereas depth images are used as input to our proposed method. For fall detection, we achieved 98.5% accuracy, 97.0% precision, 100% recall, and 98.5% F1 score.

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Hypersphere Projection-Guided Radio Frequency Fingerprinting Authentication in the Open World

Fu X., Wang Y., Lin Y., Zhang Q., Gui G., Ohtsuki T., Sari H.

IEEE Vehicular Technology Conference 2024年

ISSN  15502252

　概要を見る

In this paper, we introduce an innovative Radio Frequency Fingerprinting (RFF)-based device authentication scheme for the Internet of Things (IoT), a network marked by extensive interconnections and interactions among various entities. Our approach, designed for an open and dynamic communication environment, not only identifies devices encountered during training but also effectively rejects those not previously seen. The scheme employs a hypersphere projection for feature embedding, strategically avoiding the need to optimize intra-device variations in the radial direction. It uses a K-Means-based binary classifier for initial device assessment based on cosine similarity scores, followed by a SoftMax classifier for precise identification of known devices. Our extensive numerical analysis confirms that this method delivers superior performance, setting a new benchmark in RFF authentication for IoT security.

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