Details of a Researcher - Ohtsuki, Tomoaki

Papers - Ohtsuki, Tomoaki

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Multi-class sentiment analysis on twitter: Classification performance and challenges

Bouazizi M., Ohtsuki T.

Big Data Mining and Analytics (Big Data Mining and Analytics) 2 ( 3 ) 181 - 194 2019.09

ISSN 20960654

© 2020 The author(s). Sentiment analysis refers to the automatic collection, aggregation, and classification of data collected online into different emotion classes. While most of the work related to sentiment analysis of texts focuses on the binary and ternary classification of these data, the task of multi-class classification has received less attention. Multi-class classification has always been a challenging task given the complexity of natural languages and the difficulty of understanding and mathematically "quantifying"how humans express their feelings. In this paper, we study the task of multi-class classification of online posts of Twitter users, and show how far it is possible to go with the classification, and the limitations and difficulties of this task. The proposed approach of multi-class classification achieves an accuracy of 60.2% for 7 different sentiment classes which, compared to an accuracy of 81.3% for binary classification, emphasizes the effect of having multiple classes on the classification performance. Nonetheless, we propose a novel model to represent the different sentiments and show how this model helps to understand how sentiments are related. The model is then used to analyze the challenges that multi-class classification presents and to highlight possible future enhancements to multi-class classification accuracy.

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MUSIC-based Non-contact Heart Rate Estimation with Adaptive Window Size Setting

Yamamoto K., Toyoda K., Ohtsuki T.

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS) 6073 - 6076 2019.07

ISSN 9781538613115

© 2019 IEEE. Continuous HR (Heart Rate) monitoring enables the stress estimation in daily life. A Doppler sensor could be a key device to facilitate the non-contact HR estimation. As one of the Doppler sensor-based HR estimation methods, we have previously proposed a MUSIC (MUltiple SIgnal Classification)-based HR estimation method. MUSIC is the algorithm widely used as a tool to estimate DOA (Direction of Arrival). In our previous method, MUSIC spectrum is calculated in each sliding window, and then HR is estimated by the maximum peak detection over the MUSIC spectrum. However, when HR changes largely within the window, several peaks due to heartbeats appear over the MUSIC spectrum, which might cause the incorrect peak detection. Hence, an adaptive window is required so that only one peak appears. In this paper, we propose a MUSIC-based HR estimation method with an adaptive window size setting. When several peaks due to heartbeats appear over the MUSIC spectrum, our proposed method shortens the time window and re-calculates the MUSIC spectrum, which is repeated until only one peak appears. The experimental results showed that our method outperformed not only our previous one but also the other existing MUSIC-based HR estimation one in terms of the estimation accuracy of the HR, the stress indexes CVI (Cardiac Vagal Index) and CSI (Cardiac Sympathetic Index).

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Overloaded Wireless MIMO Switching for Secure Wireless Communication Exchanging to Untrusted Relay

Takahashi A., Sasamori F., Handa S., Takyu O., Fujii T., Ohtsuki T.

International Conference on Ubiquitous and Future Networks, ICUFN (International Conference on Ubiquitous and Future Networks, ICUFN) 2019-July 121 - 125 2019.07

ISSN 9781728113395

© 2019 IEEE. Wireless MIMO Switch makes the information exchange among terminals by two time steps. As a result, the highly efficiency of information exchanging is achieved. If the relay is impersonated by eavesdropper, it can exploit the information during the relay process, where the relay exploiting the information is the untrusted relay. In the proposed wireless MIMO switch, the stations whose number is the number of antennas plus one access the relay. As a result, the relay station hardly decodes each information owing to the overloaded access. In each station, firstly, one information signal is decoded by the interference cancellation of its own signal and the zero forcing of relay filter. Since one information signal is shared to all the stations, each station can demodulate the information because the overloaded condition is mitigated by the shared information signal. We confirm the effect of proposed scheme by computer simulation.

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Novel CA-CFAR Approach for Improvement of Doppler Sensor-based Heart Rate Variability Estimation

Hiromatsu R., Yamamoto K., Toyoda K., Ohtsuki T.

ISSN 9781538613115

© 2019 IEEE. Heart rate variability is one of major physiological parameters to reflect our stress, which has motivated researchers to investigate a Doppler sensor-based non-contact heartbeat interval estimation algorithm. As one of such methods, we have previously proposed a spectrogram-based method. In this method, the spectrum that might be due to heartbeats is integrated over a spectrogram, and then heartbeat interval is estimated by detecting peaks over the integrated spectrum. However, when a subject moves, undesired peaks with large amplitude appear, which causes the incorrect peak detection. As one of the technique to eliminate the undesired peaks with large amplitude, there is CA-CFAR (Cell Average-Constant False Alarm Rate). CA-CFAR is the technique to detect a signal, when the amplitude of a signal exceeds a threshold calculated with average amplitude of signals before and after the investigated one. However, depending on the duration of body movements, the influence of body movements might be included within the signals used for the threshold calculation, which might results in the detection failure of undesired peaks. This is because the length of GT (Guard Time) is fixed, where GT is the time to prevent the signal used for the threshold calculation from including the investigated signal components. To solve this problem, we propose a novel CA-CFAR in which the length of GT is set as the latest peak interval and only the signal before the investigated one is used so that the influence of body movements does not affect the threshold calculation. Through the experiments where a subject moves, i.e., typing, we confirmed that our spectrogram-based heart rate variability estimation method with the proposed CA-CFAR outperformed the one with CA-CFAR based on fixed GT by the RMSE (Root Mean Squared Error) between the estimated heartbeat interval and the ground truth value of the one.

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Performance Evaluation of Information-Sharing Scheme among Multiple Destinations with Mirroring Null Steering in Single Antenna Networks

Noguchi T., Takyu O., Fujii T., Ohtsuki T.

International Conference on Information Networking (International Conference on Information Networking) 2019-January 417 - 420 2019.05

ISSN 9781538683507

© 2019 IEEE. This paper clarifies the secure capacity in a secure information sharing among multiple destinations by using artificial noise and mirroring null steering (MNS) constructed by cooperative relays in single antenna networks. The MNS can selectively pass the desired signal to all destinations and remove artificial noise. Because the destinations archive high-quality received signals and the other systems cannot, secure information sharing can be achieved from a physical layer security perspective.

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Identification of Darknet Markets' Bitcoin Addresses by Voting Per-Address Classification Results

Kanemura K., Toyoda K., Ohtsuki T.

ICBC 2019 - IEEE International Conference on Blockchain and Cryptocurrency (ICBC 2019 - IEEE International Conference on Blockchain and Cryptocurrency) 154 - 158 2019.05

ISSN 9781728113289

© 2019 IEEE. Bitcoin is a decentralized digital currency whose transactions are recorded in a common ledger, so called blockchain. Due to the anonymity and lack of law enforcement, Bitcoin has been misused in darknet markets which deal with illegal products, such as drugs and weapons. Therefore from the security forensics aspect, it is demanded to establish an approach to identify newly emerged darknet markets' transactions and addresses. In this paper, we thoroughly analyze Bitcoin transactions and addresses related to darknet markets and propose a novel identification method of darknet markets' addresses. To improve the identification performance, we propose a voting based method which decides the labels of multiple addresses controlled by the same user based on the number of the majority label. Through the computer simulation with more than 200K Bitcoin addresses, it was shown that our voting based method outperforms the nonvoting based one in terms of precision, recal, and F1 score. We also found that DNM's addresses pay higher fees than others, which significantly improves the classification.

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Underdetermined Blind Separation using Multi-Subspace Representation in Time-Frequency Domain

Wang L., Ohtsuki T.

IEEE International Conference on Communications (IEEE International Conference on Communications) 2019-May 2019.05

ISSN 9781538680889

© 2019 IEEE. Blind source separation (BSS) is a technique to recognize the multiple talkers from the multiple observations received by some sensors without any prior knowledge information. The problem is that the mixing is always complex, i.e., nonlinear, underdetermined mixture, such as the case where sources are mixed with some direction angles, or where the number of sensors is less than that of sources. In this paper, we propose a multi-subspace representation based BSS approach that allows the mixing process to be nonlinear and underdetermined. The approach relies on a multi-layer representation and sparse representation in time-frequency (TF) domain. By parameterizing such subspaces, we can map the observed signals in the feature space with the coefficient matrix from the parameter space. We then exploit the linear mixture in the feature space that corresponds to the nonlinear mixture in the input space. Once such subspaces are built, the coefficient matrix can be constructed by solving an l1-regularization on the coding coefficient vector. Relying on the TF representation, the target matrix can be constructed in a sparse mixture TF vectors with a fewer computational cost. The experiments are run on the observations that are generated from nonlinear functions, and that are collected with some direction angles in a virtual room environment. The proposed approach exhibits a higher separation accuracy than that of the conventional algorithms.

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Spectrogram-Based Simultaneous Heartbeat and Blink Detection using Doppler Sensor

Yamamoto K., Toyoda K., Ohtsuki T.

IEEE International Conference on Communications (IEEE International Conference on Communications) 2019-May 2019.05

ISSN 9781538680889

© 2019 IEEE. Heartbeats and a blink are two major physiological signals that provide crucial information on our stress and drowsiness. Hence, it is highly demanded to detect simultaneously heartbeats and blinks in many applications. A Doppler sensor could be a key device to facilitate the non-contact heartbeat and blink detection in daily life. Although many Doppler sensor-based heartbeat and blink detection methods have been independently proposed, when heartbeats and blinks are detected simultaneously with one Doppler sensor, the detection accuracies of such heartbeat and blink detection methods get degraded because of at least two issues: (i) the low SNR (Signal-to-Noise Ratio) of each signal reflected from a subject's chest and face, and (ii) the similarity of the spectrum distribution of heartbeats and a blink. In this paper, we propose a spectrogram-based simultaneous heartbeat and blink detection using one Doppler sensor. In the proposed method, to extract the spectra that might be due to heartbeats and a blink, the spectra on a spectrogram are integrated. Blink detection is then performed by classifying the peaks of the integrated spectrum into a peak due to a blink or a non-blink based on a supervised machine learning classifier trained with a set of the time domain and the time-frequency domain features. Based on the non-blink peaks, heartbeats are detected considering the RRI (R-R Interval) estimated before the investigated peak to prevent the incorrect heartbeat detection. Through the experiments in the case where microwaves are transmitted from one Doppler sensor toward a body including a chest and a face, our proposal has been shown to be able to simultaneously detect heartbeats and blinks with high accuracy.

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Secrecy Performance Analysis of Wireless Sensor Networks

Ai Y., Cheffena M., Ohtsuki T., Zhuang H.

IEEE Sensors Letters (IEEE Sensors Letters) 3 ( 5 ) 2019.05

© 2017 IEEE. The wireless sensor network (WSN) plays a fundamentally important role in the realization of Internet of Things and Industry 4.0. In this article, we study the physical layer security performance of a large-scale ad hoc and homogeneous WSN. To accurately evaluate the secrecy performance of the large-scale WSN, we take into account the randomness of the interfering nodes (both in terms of the number of nodes and their respective position) based on Poisson point process in our analysis. We derive novel expressions for the average secrecy capacity and secrecy outage probability, including the effects of fading, path loss, and the network's spatial randomness. The results demonstrate the impacts of several factors such as node intensity and transmission power in large-scale WSN on secrecy performance, which should be seriously taken into consideration while designing WSNs.

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Precoding Aided Generalized Spatial Modulation with K Transmit Antenna Groups

Cao Y., Ohtsuki T.

IEEE International Conference on Communications (IEEE International Conference on Communications) 2019-May 2019.05

ISSN 9781538680889

© 2019 IEEE. In this paper, we divide antennas at the transmitter into K transmit antenna groups (TAGs) and extend the generalized spatial modulation (GSM) idea to a new precoding aided multiple-input multiple-output (MIMO) system, which is referred to as precoding aided GSM (PGSM) system. To select the receive antenna subset with low complexity, a class of efficient receive antenna selection techniques with low computational complexity are devised. To reduce the complexity of maximum likelihood (ML) detector, a low complexity iterative greedy (IG) algorithm relied ML (IG-ML) detector is also derived. Simulation results show that the bit error rate (BER) performance gain up to 3.5 dB is obtained by the proposed PGSM with IG-ML detector over the PGSM with ordered block minimum mean-squared error (OBMMSE) detector at a given BER of 10-4. It is also shown that the proposed PGSM with IG-ML detector achieves a similar BER performance in high signal-to-noise power ratio (SNR) region compared to that of an ideal precoding for the GSM with optimal ML detector.

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Polynomial Networks Representation of Nonlinear Mixtures with Application in Underdetermined Blind Source Separation

Wang L., Ohtsuki T.

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings) 2019-May 3687 - 3691 2019.05

ISSN 9781479981311

© 2019 IEEE. Similar to the deep architectures, a novel multi-layer architecture is used to extend the linear blind source separation (BSS) method to the nonlinear case in this paper. The approach approximates the nonlinearities based on a polynomial network, where the layer of our network begins with the polynomial of degree 1, up to build an output layer that can represent data with a small bias by a good approximate basis. Relying on several transformations of the input data, with higher-level representation from lower-level ones, the networks are to fulfill a mapping implicitly to the high-dimensional space. Once the polynomial networks are built, the coefficient matrix can be estimated by solving an l1-regularization on the coding coefficient vector. The experiment shows that the proposed approach exhibits a higher separation accuracy than the comparison algorithms.

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Non-Negative Matrix Factorization-Based Blind Source Separation for Non-Contact Heartbeat Detection

Ye C., Toyoda K., Ohtsuki T.

IEEE International Conference on Communications (IEEE International Conference on Communications) 2019-May 2019.05

ISSN 9781538680889

© 2019 IEEE. Recently, through exploiting the spectral sparsity of heartbeat component, a heartbeat detection method using a stochastic gradient approach has enabled a high-resolution of heartbeat spectrum reconstruction by Doppler radar signal, which also suppresses the residual noises after signal decomposition. However, the interference from respiration and/or body motion often corrupts the decomposition of signal by singular spectrum analysis (SSA), resulting in an inaccurate extraction of heartbeat component. In this paper, a non-negative matrix factorization (NMF)-based blind source separation (BSS) is first applied to non-contact heartbeat detection for better heartbeat extraction, incorporating the stochastic gradient approach. Specifically, motion noise is taken into account as one of sources, achieving relatively stable separation in various scenarios. In our proposed BSS approach, the spectrogram originated from radar signal is decomposed twice by NMF, which is used to learn the basis spectra (BS) relying on spectral correlation. Experimental results showed the improved accuracy and robustness of our method over conventional methods, on the heart rate (HR) measurement against subjects' sitting still or typewriting.

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Optimal Sleep Wakeup Mechanism for Green Internet of Things

Panahi F.H., Panahi F.H., Heshmati S., Ohtsuki T.

ICEE 2019 - 27th Iranian Conference on Electrical Engineering (ICEE 2019 - 27th Iranian Conference on Electrical Engineering) 1659 - 1663 2019.04

ISSN 9781728115085

© 2019 IEEE. The presence of many battery-powered sensors in the Internet of Things (IoT) paradigm calls for the design of green and energy-aware protocols. One of the major challenges in the wireless networks based on IoT is to reduce energy consumption and increase network lifetime. This paper addresses the problem of designing a smart sleep/wake-up mechanism using the genetic algorithm (GA). GA, as a nature-inspired optimization approach, is used to manage the operation modes of the base stations (BSs) according to the time-variant profile of the daily traffic load. Accordingly, when a number of stations are sending data to nodes, i.e., active or awake nodes, other nodes are in inactive (sleep) mode. In this case, the energy efficiency (EE) of the network increases to an acceptable level via controlling the operation modes of the densely deployed BSs over wireless sensor networks (WSNs).

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A Convergence and Asymptotic Analysis of Nonlinear Separation Model

Wang L., Ohtsuki T.

2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings (2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings) 963 - 971 2019.03

ISSN 9789881476852

© 2018 APSIPA organization. Nonlinear blind source separation is the process of estimating either the original signals or mixture functions from the degraded signals, without any prior information about original sources. The key idea is to recover the sources by estimating an approximation function so as to approximate the inverse of mixing function. However, in practice, the approximation function is derived from some estimation algorithm with finite sample size, which leads to the performance loss. In this paper, we work on the convergence and asymptotic analysis of the separation approach, which uses the flexible approximation to extract the nonlinearity of mixture function so that to make the problem linearly separable. The analysis stems from the performance of a mismatched estimator that accesses the finite sample size. By providing a closed-form expression of normalized mean squared error (NMSE), we can present a novel algebraic formalization that leads to the upper bound on the estimation error. The simulation results show that if the flexible approximation can extract the nonlinearity of mixing functions, the minimized NMSE can be achieved as the sample size tends to be infinity. This implies that the algorithm is feasible to separate the distortion of the nonlinear mixture.

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Time series analysis for bitcoin transactions: The case of Pirate@40's HYIP scheme

Toyoda K., Ohtsuki T., Mathiopoulos P.

IEEE International Conference on Data Mining Workshops, ICDMW (IEEE International Conference on Data Mining Workshops, ICDMW) 2018-November 151 - 155 2019.02

ISSN 9781538692882

© 2018 IEEE. Due to the increased popularity of Bitcoin, many researchers have analyzed how Bitcoin is being used based on the transaction history. However, the existing works analyze the transaction history in a 'static' manner and none of them analyzes transaction history 'dynamically', i.e. without taking into account the 'time variation of how Bitcoin is transferred'. The time analysis is in great demand for many practical cases, such as digital forensics tool that infers what was going on behind the scene of a fraudulent scam, and real-time inference of marketplace sales. In this paper, we propose a novel time series analysis for analyzing the history of Bitcoin transactions. In fact the main goal of our research is to detect changing points, namely anomaly detection, against a given (Bitcoin) address's transaction history. To show the effectiveness of the proposed approach, it is tested against the transaction history of Pirate@40's HYIP (High Yielding Investment Program) scheme, which raised 700,000 BTC from his investors and was charged by the Security and Exchange Commission (SEC) in 2013. It is shown that the proposed approach can successfully detect several remarkable points of Pirate@40's HYIP scheme, such as when its program's name was changed to Bitcoin Saving & Trust and when its investment rule was changed.

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Underdetermined Blind Source Separation with Multi-Subspace for Nonlinear Representation

Wang L., Ohtsuki T.

IEEE Access (IEEE Access) 7 84545 - 84557 2019

© 2013 IEEE. Blind source separation (BSS) is a technique to recognize the multiple talkers from the multiple observations received by some sensors without any prior knowledge information. The problem is that the mixing is always complex, such as the case where sources are mixed with some direction angles, or where the number of sensors is less than that of sources. In this paper, we propose a multi-subspace representation based BSS approach that allows the mixing process to be nonlinear and underdetermined. The approach relies on a multi-subspace structure and sparse representation in the time-frequency (TF) domain. By parameterizing such subspaces, we can map the observed signals in the feature space with the coefficient matrix from the parameter space. We then exploit the linear mixture in the feature space that corresponds to the nonlinear mixture in the input space. Once such subspaces are built, the coefficient matrix can be constructed by solving an optimization problem on the coding coefficient vector. Relying on the TF representation, the target matrix can be constructed in a sparse mixture of TF vectors with the fewer computational cost. The experiments are designed on the observations that are generated from an underdetermined mixture, and that is collected with some direction angles in a virtual room environment. The proposed approach exhibits higher separation accuracy.

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Multiple Radios for Fast Rendezvous in Heterogeneous Cognitive Radio Networks

Li A., Han G., Ohtsuki T.

IEEE Access (IEEE Access) 7 37342 - 37359 2019

© 2013 IEEE. In cognitive radio networks (CRNs), if two unlicensed secondary users (SUs) want to communicate with each other, they need to rendezvous with each other on the same channel at the same time. Rendezvous is the first key step for SUs to be able to communicate with each other. Channel hopping (CH) is a representative technique to solve the rendezvous problem in CRNs. SUs equipped with multiple radios can significantly reduce the time-to-rendezvous (TTR) for several existing CH algorithms while the additional cost is low. However, several drawbacks exist in the existing CH algorithms based on multiple radios. One of the main drawbacks is that they cannot be well applied in heterogeneous CRNs. The reason is that the number of radios for different SUs is implicitly assumed same or must be more than one in the existing CH algorithms based on multiple radios, which is unrealistic for heterogeneous CRNs. In heterogeneous CRNs, SUs may be equipped with different numbers of radios including one radio. To mainly address the above issue, hybrid radios rendezvous (HRR) algorithm is proposed in this paper. Moreover, the upper bounds of maximum TTR (MTTR) for the HRR algorithm are derived by a theoretical analysis. Furthermore, extensive simulations are performed to evaluate the expected TTR (ETTR), the MTTR, and the channel qualities of the rendezvous channels for the HRR algorithm. Simulation results show that rendezvous can be guaranteed by the HRR algorithm in heterogenous CRNs. Besides, the qualities of the rendezvous channels can be improved by the HRR algorithm. In addition, our algorithms can achieve rendezvous faster than several existing algorithms.

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Estimation of stress condition of children by nasal skin temperature

Suzuki M., Ohtsuki T.

Advances in Intelligent Systems and Computing (Advances in Intelligent Systems and Computing) 857 1351 - 1358 2019

ISSN 9783030011765

© Springer Nature Switzerland AG 2019. In Japan a lot of children stay home alone after school. Safety of children is a big issue. In addition, a stability of their emotion is a big issue. For the stability of their emotion, it is important to know the psychological state of children, particularly stress so that we can try to relieve their stress. As for the detection of the stress, it is preferred to detect in a less constrained or non-contact way. The nasal skin temperature is known to be affected by emotion, which can be detected in a non-contact way with an infrared thermography camera. In this paper, we report our experimental results about the detection of stress condition of children by the nasal skin temperature with the infrared thermography camera. In estimating the stress condition, we use a heart rate variability as a physiological index. With our experimental results, we show that the stress condition of children can be estimated by the nasal skin temperature. We also show that when the stress index rises, the change of the nasal skin temperature differs depending on their ages.

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Doppler Sensor-Based Blink Duration Estimation by Analysis of Eyelids Closing and Opening Behavior on Spectrogram

Yamamoto K., Toyoda K., Ohtsuki T.

IEEE Access (IEEE Access) 7 42726 - 42734 2019

© 2013 IEEE. Blink duration is one of the useful indicators to estimate drowsiness and fatigue. A Doppler sensor could be a key device to realize the non-contact blink duration estimation, which is very useful for the drowsiness and fatigue monitoring in real life. However, none of the blink duration estimation methods has been proposed so far. In this paper, we propose a novel Doppler sensor-based blink duration estimation method based on the analysis of eyelids closing and opening behavior on a spectrogram. When one blinks, the energies caused by eyelids closing and opening behavior appears on a spectrogram. Hence, the blink duration can be estimated by integrating such energies and then detecting the timings when the integrated energy caused by the eyelids closing behavior appears and when the energy caused by the eyelids opening behavior disappears. To precisely detect such timings, a spectrogram with a short time window is better. However, thorough the preliminary experiments, we confirmed that when a shorter time window is used, more noise appears around such timings, which makes it difficult to detect such timings. To deal with this issue, the proposed method multiplies several integrated energies for spectrograms with different time windows. By multiplying the integrated energies with such noise and the ones without such noise, the energy level of such noise gets significantly declined over the product. Furthermore, the levels of the redundant blink energies also decline to the noise level, where the redundant blink energies denote the ones that appear before and after the eyelids closing and opening timings because of a long time window. The results of the experiments on 8 subjects watching a movie showed that our method achieved an average root mean squared error (RMSE) of 49.3 ms.

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A Novel Methodology for HYIP Operators' Bitcoin Addresses Identification

Toyoda K., Takis Mathiopoulos P., Ohtsuki T.

IEEE Access (IEEE Access) 7 74835 - 74848 2019

© 2013 IEEE. Bitcoin is one of the most popular decentralized cryptocurrencies to date. However, it has been widely reported that it can be used for investment scams, which are referred to as high yield investment programs (HYIP). Although from the security forensic point of view it is very important to identify the HYIP operators' Bitcoin addresses, so far in the open technical literature no systematic method which reliably collects and identifies such Bitcoin addresses has been proposed. In this paper, a novel methodology is introduced, which efficiently collects a large number of the HYIP operators' Bitcoin addresses and identifies them based upon a novel analysis of their transactions history. In particular, a scraping-based method is first proposed which is able to collect more than 2,000 HYIP operators' Bitcoin addresses from the Internet thus providing a large number of the HYIPs' samples. Second, a supervised machine learning technique, which classifies, whether or not, specific Bitcoin addresses belong to the HYIP operators, is introduced and its performance is evaluated. The proposed classification method is based upon two novel approaches, namely the rate conversion technique that mitigates the effect of Bitcoin price volatility and the sampling technique that reduces the computational amount without sacrificing the classification performance. By employing close to 30,000 real Bitcoin addresses, extensive performance evaluation results obtained by means of computer simulation experiments have shown that the proposed methodology achieves excellent performance, i.e., 95% of the HYIP addresses can be correctly classified, while maintaining a false positive rate less than 4.9%. In order to further validate the proposed classifier's ability to detect the HYIP operators' Bitcoin addresses, our designed classifier has been tested against a recently published list of the HYIP addresses maintaining its excellent detection accuracy by achieving a 93.75% success rate.

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