研究者詳細 - 荒木　俊輔

論文 - 荒木　俊輔

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Synchronization of Chaotic Extremum-Coded Random Number Generators and Its Application to Segmented Image Encryption 査読有り国際誌

Araki S., Wu J.H.(National Chin-Yi University of Technology), Yan J.J.(National Chin-Yi University of Technology)

Mathematics 12 ( 19 ) 2024年10月

担当区分：筆頭著者記述言語：英語掲載種別：研究論文（学術雑誌）

This paper proposes a highly secure image encryption technique based on chaotic synchronization. Firstly, through the design of a synchronization controller, we ensure that the master–slave chaotic extremum-coded random number generators (ECRNGs) embedded in separated transmitters and receivers are fully synchronized to provide synchronized dynamic random sequences for image encryption applications. Next, combining these synchronized chaotic sequences with the AES encryption algorithm, we propose an image segmentation and multi-encryption method to enhance the security of encrypted images and realize a secure image transmission system. Notably, in the design of the synchronization controller, the transient time before complete synchronization between the master and slave ECRNGs is effectively controlled by specifying the eigenvalues of the matrix in the synchronization error dynamics. Research results in this paper also show that complete synchronization of ECRNGs can be achieved within a single sampling time, which significantly contributes to the time efficiency of the image transmission system. As for the image encryption technique, we propose the method of image segmentation and use the synchronized dynamic random sequences generated by the ECRNGs to produce the keys and initialization vectors (IVs) required for AES-CBC image encryption, greatly enhancing the security of the encrypted images. To highlight the contribution of the proposed segmented image encryption, statistical analyses are conducted on the encrypted images, including histogram analysis (HA), information entropy (IE), correlation coefficient analysis (CCA), number of pixels change rate (NPCR), and unified average changing intensity (UACI), and compared with existing literature. The comparative results fully demonstrate that the proposed encryption method significantly enhances image encryption performance. Finally, under the network transmission control protocol (TCP), the synchronization of ECRNGs, dynamic keys, and IVs is implemented as well as segmented image encryption and transmission, and a highly secure image transmission system is realized to validate the practicality and feasibility of our design.

DOI： 10.3390/math12192983

Scopus

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A Deployment of Ensemble-Based IDS on Raspberry Pi 査読有り国際誌

Musthafa M.B., Huda S., Shamim S.M., Kodera Y., Mwaura J., Araki S., Nogami Y.

GCCE 2024 - 2024 IEEE 13th Global Conference on Consumer Electronics 184 - 185 2024年10月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Intrusion Detection Systems (IDS) are essential for safeguarding IoT networks against various attacks. Our previously developed ensemble-based IDS model, which combines stacked Long Short-Term Memory (LSTM) networks with ANOVA feature selection, achieved a remarkable accuracy of 97.46% and an overfitting value of 0.22% in classifying network attacks. However, deploying this powerful model on resource-constrained devices like Raspberry Pi poses challenges due to limited computational resources. In this paper, we tackle these challenges by converting the original model to TensorFlow Lite (TFLite) and applying quantization techniques to optimize the model size for deployment on a Raspberry Pi 3b+. Our approach significantly reduces the model size by 90.9% while maintaining an impressive accuracy of 96.96%, only a 0.5% reduction from the original model. The optimized model demonstrates efficient performance on the Raspberry Pi, with a loading time of 100.2ms and an inference time of 1.2ms.

DOI： 10.1109/GCCE62371.2024.10760989

Scopus

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Relation between Recognition Accuracy and Object's Occupancy in CAPTCHA Image Using Weave Filters 査読有り国際誌

Hayashi N., Miyazaki T., Araki S., Uehara S.

11th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2024 401 - 402 2024年07月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Taiwan Taichung 2024年07月09日 - 2024年07月11日

We recently proposed a CAPTCHA with images processed by a weave filter. This is more human-friendly and harder on computers than the previous CAPTCHA. However, some of these images are likely to be answered incorrectly by humans or to be judged correctly by the program. We think that the cause of the problem is the object's occupancy rate in an imageIn this paper, we first briefly explain our proposed method. Next, we evaluate inputs of raw and processed images into YOLOv5 and organize the results in terms of the object's occupancy rate in an image and recognition accuracy. We then analyzed the results to find out what kind of images humans and YOLOv5 are good or bad at recognizing.

DOI： 10.1109/ICCE-Taiwan62264.2024.10674533

Scopus

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Optimizing IoT Intrusion Detection Using Balanced Class Distribution, Feature Selection, and Ensemble Machine Learning Techniques 査読有り国際誌

Musthafa M.B., Huda S., Kodera Y., Ali M.A., Araki S., Mwaura J., Nogami Y.

Sensors 24 ( 13 ) 2024年07月

記述言語：英語掲載種別：研究論文（学術雑誌）

Internet of Things (IoT) devices are leading to advancements in innovation, efficiency, and sustainability across various industries. However, as the number of connected IoT devices increases, the risk of intrusion becomes a major concern in IoT security. To prevent intrusions, it is crucial to implement intrusion detection systems (IDSs) that can detect and prevent such attacks. IDSs are a critical component of cybersecurity infrastructure. They are designed to detect and respond to malicious activities within a network or system. Traditional IDS methods rely on predefined signatures or rules to identify known threats, but these techniques may struggle to detect novel or sophisticated attacks. The implementation of IDSs with machine learning (ML) and deep learning (DL) techniques has been proposed to improve IDSs’ ability to detect attacks. This will enhance overall cybersecurity posture and resilience. However, ML and DL techniques face several issues that may impact the models’ performance and effectiveness, such as overfitting and the effects of unimportant features on finding meaningful patterns. To ensure better performance and reliability of machine learning models in IDSs when dealing with new and unseen threats, the models need to be optimized. This can be done by addressing overfitting and implementing feature selection. In this paper, we propose a scheme to optimize IoT intrusion detection by using class balancing and feature selection for preprocessing. We evaluated the experiment on the UNSW-NB15 dataset and the NSL-KD dataset by implementing two different ensemble models: one using a support vector machine (SVM) with bagging and another using long short-term memory (LSTM) with stacking. The results of the performance and the confusion matrix show that the LSTM stacking with analysis of variance (ANOVA) feature selection model is a superior model for classifying network attacks. It has remarkable accuracies of (Formula presented.) and (Formula presented.) and overfitting values of (Formula presented.) and (Formula presented.) on the two datasets, respectively. The model’s ROC is also shaped with a sharp bend, with AUC values of (Formula presented.) and (Formula presented.) for the UNSW-NB15 dataset and the NSL-KD dataset, respectively.

DOI： 10.3390/s24134293

Scopus

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A Study on a New Pseudorandom Number Generator with Deterministic Maze Construction 査読有り国際誌

Miyazaki T., Araki S., Uehara S.

11th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2024 393 - 394 2024年07月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

2024年07月09日 - 2024年07月11日

In this paper, we propose a new pseudorandom number generator (PRNG), which uses a new deterministic maze construction for our maze-based PRNG [1]. We evaluate some relations between a size and a number of shapes for the mazes generated by our maze construction. Then, we also estimate a number of shapes and the maximum duplicate number of mazes by the size of the maze. By using the asymptotic equations of these estimated values, we can derive the minimum required maze size for the required number of sequences in which most or all sequences are different.

DOI： 10.1109/ICCE-Taiwan62264.2024.10674438

Scopus

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A Novel Design of Random Number Generators Using Chaos-Based Extremum Coding 査読有り

Shunsuke Araki, Ji-Han Wu, Jun-Juh Yan(National Chin-Yi University of Technology)

IEEE Access ( IEEE ) 12 24039 - 24047 2024年02月

記述言語：英語掲載種別：研究論文（学術雑誌）

This paper proposes a new chaos-based extremum coding method to realize a true random number generator (RNG). Based on the chain rule, we innovatively introduce two parameters into the dynamics of chaotic systems to modulate the speed and amplitude of state responses. Then, by discretizing the continuous modulated chaotic system, the corresponding discrete chaotic system can be obtained, which allows the use of low-cost micro-controllers for implementation, enhancing system stability, and reducing costs. Also, a novel chaos-based extremum coding approach is proposed for generating a random extremum-coded sequence (RECS). Using this RECS to switch and decide modulation parameters significantly improves the randomness of the sequences generated by the RNGs. To highlight the contribution of this RNG design, the randomness and security of the RNGs are evaluated by statistical tests such as NIST, Diehard, and ENT. Through comparisons with recent published works, the results show that the proposed chaotic extremum-coded RNG can demonstrate superior performance with a higher level of randomness.

DOI： 10.1109/ACCESS.2024.3365638

Kyutacar

A Study on DDoS Attacks Detection on IoT Devices Using Machine Learning for Microcontrollers 査読有り国際誌

Jedidah M Mwaura,Shunsuke Araki,Ken'ichi Kakizaki

Proc. of IEEE 42nd International Conference on Consumer Electronics 1 - 4 2024年01月

担当区分：責任著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

USA Las Vegas 2024年01月05日 - 2024年01月08日

In recent years, there has been a proliferation of Internet of Things (IoT) devices, and so has been the attacks on them. In this paper we will propose a methodology to detect Distributed Denial of Service (DDoS) attacks on IoT devices using Machine Learning for Microcontrollers or TinyML. We will discuss a model which we made for Arduino Nano 33 BLE Sense using Machine Learning for Microcontrollers or TinyML. Additionally, we will discuss results of our proposal in detecting DDoS attacks on IoT devices. Lastly, we will describe the feasibility of our model on IoT devices.

Some Properties of Well-Balanced Sequences Obtained from Two Logistic Maps over Integers 査読有り国際誌

H. Fujii, S. Uehara, T. Miyazaki, S. Araki, Y. Nogami

2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings 525 - 526 2023年07月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Taiwan Ping Tung 2023年07月17日 - 2023年07月19日

In this paper, we introduce a method for generating well-balanced sequences by two types of the logistic maps over integers with a small arithmetic precision. We proposed this method last year. We think that the randomness and unpredictability of the sequences are caused by a simple combination of partial outputs of two symmetric mapping, however we cannot explain the reason in detail. Here, we show that the sequence has superior properties from results of typical randomness tests.

DOI： 10.1109/ICCE-Taiwan58799.2023.10226953

Scopus

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How to Construct Pseudorandom Bit Sequences from Mazes by a Method Digging Out Walls 査読有り国際誌

T. Miyazaki, S. Araki, S. Uehara

2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings 539 - 540 2023年07月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Taiwan Ping Tung 2023年07月17日 - 2023年07月19日

In this paper, we propose a new pseudorandom bit sequence generator, which is based on a method digging out walls. Although each of these sequences lacks enough randomness, synthesized sequences of them can pass all of the NIST statistical test suite. We also describe a conjectural relation between the number of types on the square mazes and their sizes.

DOI： 10.1109/ICCE-Taiwan58799.2023.10226947

Scopus

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A Study on Guessing plaintexts by Neural Network against Data Encryption Standard (DES) 査読有り国際誌

Kawano M., Peng S., Araki S., Kakizaki K.

2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings 531 - 532 2023年07月

担当区分：責任著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Taiwan Ping Tung 2023年07月17日 - 2023年07月19日

One of our research purpose is to evaluate a symmetric key encryption scheme by using a machine learning. As the first step, this paper shows results of a known plaintext attack by using the neural network against Data Encryption Standard (DES) as the symmetric key encryption scheme. A plaintext guessing attack from ciphertexts against 16-round DES did not yield good results. On the other hand, we report interesting results in their attacks against 1- and 2-round DES.

DOI： 10.1109/ICCE-Taiwan58799.2023.10226974

Scopus

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A Study on a Full-node Problem of the Domain Name System based on Ethereum 査読有り国際誌

S. Peng, S. Araki, K. Kakizaki

2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings 529 - 530 2023年07月

担当区分：責任著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Taiwan PingTung 2023年07月17日 - 2023年07月19日

Domain Name System(DNS) is a crucial component for us to access the Internet. However, it is vulnerable to several attacks such as DNS Cache Poisoning Attack and DNS DDoS Attack. Because of the high security promised by the Blockchain technology, we investigate the domain name systems based on blockchain, and focus on Ethereum Name Service(ENS). In this paper, we propose an ENS-oriented light node in order to solve the Full-node problem which means each participant has to hold a large amount data.

DOI： 10.1109/ICCE-Taiwan58799.2023.10227070

Scopus

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ランダムパターン投影によるSfM三次元計測の精度向上手法査読有り

菅野美紀, 硴崎賢一, 冨永泰正, 荒木俊輔

日本建築学会技術報告集 ( 一般社団法人日本建築学会 ) 29 ( 71 ) 533 - 537 2023年02月

記述言語：日本語掲載種別：研究論文（学術雑誌）

<p>SfM can reconstruct the 3D shape of an object with high accuracy if there are many feature points on the surface to be measured. However, in the field of architecture, there is a problem that building surfaces are beautifully preserved and the shape cannot be reconstructed because of the small number of feature points. In this study, we proposed a method to improve the measurement accuracy of SfM by projecting a random pattern on a wall surface and distributing a large number of feature points. Experiments confirmed that the measurement accuracy was as good as that of a laser scanner.</p>

DOI： 10.3130/aijt.29.533

CiNii Research

Damage Detection of the RC Building in TLS Point Clouds Using 3D Deep Neural Network 査読有り

SHAO Wanpeng, KAKIZAKI Ken'ichi, ARAKI Shunsuke, MUKAI Tomohisa

精密工学会誌 ( 公益社団法人精密工学会 ) 89 ( 1 ) 70 - 76 2023年01月

記述言語：英語掲載種別：研究論文（学術雑誌）

<p>Nowadays, it is possible to capture the whole surface areas of buildings in three-dimensional point clouds using laser scanners. However, it is still challenging to find the damaged areas of the building data automatically. The objective of the study is to investigate the use of deep neural networks in combination with the building data captured by the terrestrial laser scanner for damage detection. The post-seismic building data for our experiments is from a structural lab. To find out what kinds of DNNs are useful for damage detection, the deep neural networks we choose are PointNet and PointNet++ (MSG, MRG, and SSG). Since innovative DNNs can only directly operate on small 3D rigid models, we transform the detection problem into a classification problem by voxelizing the whole façade before feeding into the 3D DNNs. Meanwhile, we find a pair of optimal parameters after investigating the impact of different point sizes and voxel sizes on detection results. By comparison, we perform a point-based classification by Random Forest to highlight the effectiveness of our approach.</p>

DOI： 10.2493/jjspe.89.70

CiNii Research

その他リンク： https://www.jstage.jst.go.jp/article/jjspe/89/1/89_70/_pdf

Reflections Removal Produced by Multiple Transparent and Reflective Glass Objects in TLS Measurements 査読有り国際誌

Shao W., Kakizaki K., Araki S., Mukai T.

Proceedings - International Computer Software and Applications Conference 2023-June 370 - 379 2023年01月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Italy Torino 2023年06月26日 - 2023年06月30日

In the measurement of buildings using terrestrial laser scanners, the measurement data contains a large amount of noise that is erroneously measured due to reflections from reflective and transparent glass objects. Measurement data containing such reflection noise adversely affects the analysis that uses it. In this study, we propose an efficient method to remove these erroneous measurements. First, we estimate the glass regions that are the source of the reflection noise based on the reflection intensity of the measured point cloud. Next, we trace the path of the laser beams using the estimated glass regions and the laser scanner position to estimate the area where reflection noise can occur. Finally, we detect reflection noises based on a combination of (1) the symmetry of the noise point cloud and its source entity due to reflection by the window and (2) the geometric similarity of those point clouds, and only reflection noise is removed based on them. Experimental results showed that the reflection noise removal rate reached around 90%, confirming that only the reflection noise can be selectively removed from a measurement point cloud.

DOI： 10.1109/COMPSAC57700.2023.00057

Scopus

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Identification of Transparent and Specular Reflective Windows from Terrestrial Laser Scanner Data with Intensity Values 査読有り国際誌

Wanpeng Shao, Ken'ichi Kakizaki, Shunsuke Araki, Tomohisa Mukai

Proc. of 2022 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW) 1 - 2 G1(Intelligent Multimedia Processing and Analysis)-1 2022年07月

A Study on Parameters of Piecewise Logistic Map over Large Integers and Processing Time 査読有り国際誌

Ryo Inoue, Takeru Miyazaki, Shunsuke Araki, Satoshi Uehara

Proc. of 2022 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW) 1 - 2 C5(Advanced Cryptography and its Applications)-3 2022年07月

A Study on a DDH-Based Keyed Homomorphic Encryption Suitable to Machine Learning in the Cloud 査読有り国際誌

Takuya Tsuruta, Shunsuke Araki, Takeru Miyazaki, Satoshi Uehara, Ken'ichi Kakizaki

Proc. of 2022 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW) 1 - 2 C5(Advanced Cryptography and its Applications)-2 2022年07月

Inspection Support System for Aging Buildings Using Virtual Reality Technology 査読有り国際誌

Wanpeng Shao, Ayumu Sasaki, Ken'ichi Kakizaki, Shunsuke Araki, Tomohisa Mukai

Proc. of 2022 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW) 1 - 2 A5(Virtual Reality, Augmented Reality, and Displays)-1 2022年07月

Improving Motivation Control in Personal Software Process Education Course with a Qualitative Approach 査読有り国際誌

Shigeru Kusakabe, Shunsuke Araki, Keiichi Katamine, Masanobu Umeda

International Journal of Learning Technologies and Learning Environments, International Institute of Applied Informatics ( 特定非営利活動法人国際応用情報学研究機構 ) 5 ( 1 ) 1 - 14 2022年02月

高密度計測による計測値の真値推定とニ時期の計測データの差分算出による高精度三次元レーザー計測法査読有り

Kakizaki K., Mukai T., Araki S.

AIJ Journal of Technology and Design ( 一般社団法人日本建築学会 ) 28 ( 68 ) 515 - 520 2022年02月

記述言語：日本語掲載種別：研究論文（学術雑誌）

3D laser measurement has a problem that the measurement precision is not sufficient for damage analysis of buildings. In addition, when there is an unevenness in the measurement target, it is difficult to distinguish whether it is an initial shape or damage. In this paper, we propose a method to reduce the measurement error by processing the high-density laser measurement data with statistical methods. In addition, we propose a method to detect the damaged area by comparing the initial shape data of the measured object with the newly measured data.

DOI： 10.3130/aijt.28.515

Scopus

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CiNii Research

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