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Switch Capacitor-Based time-varying transfer function for FCN and CNN MLSCA-Resistant AES256 in 65-nm CMOS

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2024年第1期71卷 405-409页

作者： Ghosh, Archisman Das, Debayan Ghosh, Santosh Sen, Shreyas Purdue Univ Dept Elect & Comp Engn W Lafayette IN 47097 USA Indian Inst Sci Elect Syst Engn Bengaluru 560012 India Intel Corp Secur & Privacy Res Dept Hillsboro OR 97124 USA

Mathematically secure cryptographic implementations can leak critical information through physical side channels. Machine learning (ML) has facilitated efficient side-channel analysis (SCA), especially on small IoT devices and smart cards. We propose a lightweight, synthesizable technique to enhance ML-based SCA resilience. Our approach introduces a physical time variance technique that specifically targets Deep Neural Network based MLSCA. This brief presents a physical time variance technique that is effective against CNN contrary to the previous state-of-the-art. By eliminating analog units and utilizing a switched capacitor design, it outperforms existing techniques by 5x in terms of traces to train the attacking neural network.

关键词： Capacitors Switches time-domain analysis transfer functions Training Switching circuits Side-channel attacks Side channel attack countermeasures AES256 time-varying transfer function generic low-overhead synthesizable MLSCA

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Physical time-varying transfer function as Generic Low-Overhead Power-SCA Countermeasure

IEEE OPEN JOURNAL OF CIRCUITS AND SYSTEMS

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IEEE OPEN JOURNAL OF CIRCUITS AND SYSTEMS 2023年 4卷 228-240页

作者： Ghosh, Archisman Das, Debayan Sen, Shreyas Purdue Univ W Lafayette IN 47907 USA Indian Inst Sci Bengaluru 560012 India

Mathematically secure cryptographic algorithms leak significant side-channel information through their power supplies when implemented on a physical platform. These side-channel leakages can be exploited by an attacker to extract the secret key of an embedded device. The existing state-of-the-art countermeasures mainly focus on power balancing, gate-level masking, or signal-to-noise (SNR) reduction using noise injection and signature attenuation, all of which suffer either from the limitations of high power/area overheads, throughput degradation or are not synthesizable. In this article, we propose a generic low-overhead digital-friendly power SCA countermeasure utilizing a physical time-varying transfer function (TVTF) by randomly shuffling distributed switched capacitors to significantly obfuscate the traces in the time domain. We evaluate our proposed technique utilizing a MATLAB-based system-level simulation. Finally, we implement a 65nm CMOS prototype IC and evaluate our technique against power side-channel attacks (SCA). System-level simulation results of the TVTF-AES show similar to 5000x minimum traces to disclosure (MTD) improvement over the unprotected implementation with similar to 1.25x power and similar to 1.2x area overheads, and without any performance degradation. SCA evaluation with the prototype IC shows 3.4M MTD which is 500x greater than the unprotected solution.

关键词： Power side-channel attack low-overhead countermeasure physical obfuscation time-varying transfer function synthesizable generic

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Vibration Separation Methodology Compensated by time-varying transfer function for Fault Diagnosis of Non-Hunting Tooth Planetary Gearbox

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SENSORS 2022年第2期22卷 557页

作者： Tong, Shuiguang Li, Junjie Cong, Feiyun Fu, Zilong Tong, Zheming Zhejiang Univ State Key Lab Fluid Power & Mech Syst 38 Zheda Rd Hangzhou 310027 Peoples R China Zhejiang Univ Sch Mech Engn 38 Zheda Rd Hangzhou 310027 Peoples R China

Due to planetary movement of planet gears, the vibration signal perceived by a stationary sensor is modulated and difficult to diagnose. This paper proposed a vibration separation methodology compensated by a time-varying transfer function (TVTF-VS), which is a further development of the vibration separation (VS) method in the diagnosis of non-hunting tooth planetary gearboxes. On the basis of VS, multi-teeth VS is proposed to extract and synthesize the meshing signal of a planet gear using a single transducer. Considering the movement regularity of a planetary gearbox, the time-varying transfer function (TVTF) is represented by a generalized expression. The TVTF is constructed using a segment of healthy signal and an evaluation indicator is established to optimize the parameters of the TVTF. The constructed TVTF is applied to overcome the amplitude modulation effect and highlight fault characteristics. After that, experiments with baseline, pitting, and compound localized faults planet gears were conducted on a non-hunting tooth planetary gearbox test rig, respectively. The results demonstrate that incipient failure on a planet gear can be detected effectively, and relative location of the local faults can be determined accurately.

关键词： vibration separation time-varying transfer function non-hunting ratio planetary gearbox fault diagnosis

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A novel nonlinear time-varying sigmoid transfer function in binary whale optimization algorithm for descriptors selection in drug classification

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MOLECULAR DIVERSITY 2023年第1期27卷 71-80页

作者： Yusof, Norfadzlia Mohd Muda, Azah Kamilah Pratama, Satrya Fajri Abraham, Ajith Univ Tekn Malaysia Melaka Fak Teknol Kejuruteraan Elekt & Elekt Durian Tunggal 76100 Melaka Malaysia Univ Tekn Malaysia Melaka Fak Teknol Maklumat & Komunikasi Durian Tunggal 76100 Melaka Malaysia Sci Network Innovat & Res Excellence Machine Intelligence Res Labs MIR Labs Auburn WA USA

In computational chemistry, the high-dimensional molecular descriptors contribute to the curse of dimensionality issue. Binary whale optimization algorithm (BWOA) is a recently proposed metaheuristic optimization algorithm that has been efficiently applied in feature selection. The main contribution of this paper is a new version of the nonlinear time-varying Sigmoid transfer function to improve the exploitation and exploration activities in the standard whale optimization algorithm (WOA). A new BWOA algorithm, namely BWOA-3, is introduced to solve the descriptors selection problem, which becomes the second contribution. To validate BWOA-3 performance, a high-dimensional drug dataset is employed. The proficiency of the proposed BWOA-3 and the comparative optimization algorithms are measured based on convergence speed, the length of the selected feature subset, and classification performance (accuracy, specificity, sensitivity, and f-measure). In addition, statistical significance tests are also conducted using the Friedman test and Wilcoxon signed-rank test. The comparative optimization algorithms include two BWOA variants, binary bat algorithm (BBA), binary gray wolf algorithm (BGWOA), and binary manta-ray foraging algorithm (BMRFO). As the final contribution, from all experiments, this study has successfully revealed the superiority of BWOA-3 in solving the descriptors selection problem and improving the Amphetamine-type Stimulants (ATS) drug classification performance. [GRAPHICS] .

关键词： Metaheuristic Feature selection Descriptors selection time-varying transfer function Binary whale optimization algorithm

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Estimation of time-varying coherence function using time-varying transfer functions

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ANNALS OF BIOMEDICAL ENGINEERING 2005年第11期33卷 1582-1594页

作者： Zhao, H Lu, S Zou, R Ju, KW Chon, KH SUNY Stony Brook Dept Biomed Engn Stony Brook NY 11794 USA Childrens Hosp Dept Neurosurg Boston MA 02115 USA

We introduce a new method to estimate reliable time-varying coherence functions (TVCF) for causal systems. The technique is based on our previously developed method to estimate time-varying transfer functions (TVTF), known as the time-varying optimal parameter search algorithm [Zou, R., H. Wang, and K. H. Chon. A robust time-varying identification algorithm using basis functions. Ann. Biomed. Eng. 31: 840-853, 2003]. The TVCF is estimated by the multiplication of two TVTFs. The two TVTFs are obtained using signal x as the input and signal y as the output to produce the first TVTF, and signal y as the input and signal x as the output to produce the second TVTF. Demonstration of the feasibility and efficacy of the proposed approach is provided with both simulation examples and application to renal blood flow and pressure data. The proposed approach provides higher time-frequency resolution TVCF than afforded by the short time Fourier transform based TVCF.

关键词： time-varying transfer function time-varying coherence function ARMA time-varying renal autoregulation

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Improved swarm intelligence algorithms with time-varying modified Sigmoid transfer function for Amphetamine-type stimulants drug classification

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2022年 226卷

作者： Yusof, Norfadzlia Mohd Muda, Azah Kamilah Pratama, Satrya Fajri Carbo-Dorca, Ramon Abraham, Ajith Univ Teknikal Malaysia Melaka Fak Teknol Kejuruteraan Elekt dan Elekt Hang Tuah Jaya Durian Tunggal Melaka 76100 Malaysia Univ Teknikal Malaysia Melaka Fak Teknol Maklumat dan Komunikasi Hang Tuah Jaya Durian Tunggal Melaka 76100 Malaysia Univ Girona Inst Quim Computac Catalisi i Catalonia Girona 17071 Spain Machine Intelligence Res Labs MIR Labs Sci Network Auburn WA USA

Swarm-intelligence (SI) algorithms have received great attention in addressing various binary optimization problems such as feature selection. In this article, a new time-varying modified Sigmoid transfer function with two time-varying updating schemes is proposed as the binarization method for particle swarm optimization (PSO), grey wolf optimization algorithm (GWO), whale optimization algorithm (WOA), harris hawk optimization (HHO), and manta-ray foraging optimization (MRFO). The new binary algorithms, BPSO, BGWOA, BWOA, BHHO, and BMRFO algorithms are utilized for solving the descriptors selection problem in supervised Amphetamine-type Stimulants (ATS) drug classification task. The goal of this study is to improve the speed of convergence and classification accuracy. To evaluate the performance of the proposed methods, experiments were carried out on a specific chemical dataset containing molecular descriptors of ATS and non-ATS drugs. The results obtained showed that the proposed methods' performances on the chemical dataset are promising in near to optimal convergence, fast computation, increased classification accuracy, and enormous reduction in descriptor size.

关键词： Descriptors selection time-varying transfer function Binary optimization algorithm Drug classification

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Robust algorithm for estimation of time-varying transfer functions

Robust algorithm for estimation of time-varying transfer fun...

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25th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society

作者： Zou, R Chon, KH SUNY Stony Brook Dept Biomed Engn Stony Brook NY 11794 USA

ISBN: (纸本)0780377893

We introduce a new method to estimate time-varying transfer functions and time-varying impulse response functions. The method is based on time-varying autoregressive moving average models in which the time-varying parameters are accurately obtained using the time-varying optimal parameter search (TVOPS) algorithm which we have developed [1]. The TVOPS is more accurate than the recursive least squares, and remains robust even in the case of significant noise contamination. Application of the new method to renal blood pressure and flow revealed that hypertensive rats undergo more complex and time-varying autoregulation in maintaining stable blood flow than do normotensive rats. The newly-developed approach may promote the broader use of time-varying system identification in studies of physiological systems.

关键词： time-varying impulse response function time-varying transfer function system identification

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Binary QUATRE Using time-varying transfer functions

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JOURNAL OF INTERNET TECHNOLOGY 2022年第3期23卷 425-435页

作者： Chu, Shu-Chuan Zhuang, Zhongjie Hu, Chia-Cheng Pan, Jeng-Shyang Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao Peoples R China Flinders Univ S Australia Coll Sci & Engn Adelaide SA Australia Yango Univ Coll Artificial Intelligence Fuzhou Peoples R China

QUasi-Affine TRansformation Evolutionary (QUATRE) is an excellent algorithm that generalized differential evolution algorithm to matrix form. There are two approaches of Binary QUATRE (BQUATRE) algorithm that convert the continuous search space to the binary one. The convergence process of meta-heuristic algorithms can be regarded as two stages: exploration and exploitation. Generally speaking, transfer function plays an important role in the binary meta heuristic algorithms. Therefore, four families of transfer functions are converted into the time-varying form to balance the exploration and exploitation. In particular, a new time varying formula for the V-shaped, U-shaped and Z-shaped transfer functions is designed in this manuscript. Then, two new approaches of BQUATRE algorithm using the time varying transfer functions are proposed. In addition, a wrapper feature selection algorithm is designed based on the proposed time-varying BQUATRE. In order to verify the performance of the proposed algorithms, the UCI repository is used in the experiment and the results show that the proposed algorithms are superior to the original BQUATRE and the state-of-the-art algorithms.

关键词： Binary QUATRE time-varying transfer function Feature selection

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time-varying Coherence function for Atrial Fibrillation Detection

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2013年第10期60卷 2783-2793页

作者： Lee, Jinseok Nam, Yunyoung McManus, David D. Chon, Ki H. Wonkwang Univ Dept Biomed Engn Sch Med Iksan Jeonbuk South Korea Worcester Polytech Inst Dept Biomed Engn Worcester MA 01609 USA Univ Massachusetts Med Ctr Div Cardiol Dept Med Worcester MA 01605 USA Univ Massachusetts Med Ctr Dept Quantitat Hlth Sci Worcester MA 01605 USA

We introduce a novel method for the automatic detection of atrial fibrillation (AF) using time-varying coherence functions (TVCF). The TVCF is estimated by the multiplication of two time-varying transfer functions (TVTFs). The two TVTFs are obtained using two adjacent data segments with one data segment as the input signal and the other data segment as the output to produce the first TVTF;the second TVTF is produced by reversing the input and output signals. We found that the resultant TVCF between two adjacent normal sinus rhythm (NSR) segments shows high coherence values (near 1) throughout the entire frequency range. However, if either or both segments partially or fully contain AF, the resultant TVCF is significantly lower than 1. When TVCF was combined with Shannon entropy (SE), we obtained even more accurate AF detection rate of 97.9% for the MIT-BIH atrial fibrillation (AF) database (n = 23) with 128 beat segments. The detection algorithm was tested on four databases using 128 beat segments: the MIT-BIH AF database, the MIT-BIH NSR database (n = 18), the MIT-BIH Arrhythmia database (n = 48), and a clinical 24-h Holter AF database (n = 25). Using the receiver operating characteristic curves from the combination of TVCF and SE, we obtained a sensitivity of 98.2% and specificity of 97.7% for the MIT-BIH AF database. For the MIT-BIH NSR database, we found a specificity of 99.7%. For the MIT-BIH Arrhythmia database, the sensitivity and specificity were 91.1% and 89.7%, respectively. For the clinical database (24-h Holter data), the sensitivity and specificity were 92.3% and 93.6%, respectively. We also found that a short segment (12 beats) also provided accurate AF detection for all databases: sensitivity of 94.7% and specificity of 90.4% for the MIT-BIH AF, specificity of 94.4% for the MIT-BIH-NSR, the sensitivity of 92.4% and specificity of 84.1% for the MIT-BIH arrhythmia, and sensitivity of 93.9% and specificity of 84.4% for the clinical database. The advantag

关键词： Atrial fibrillation (AF) cardiac arrhythmia ECG parametric time-frequency spectra Shannon entropy (SE) short physiological time series time-varying coherence function time-varying transfer function

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A non-parametric approach for linear system identification using principal component analysis

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2007年第4期21卷 1576-1600页

作者： Tan, M. H. Hammond, J. K. Univ Southampton Inst Sound & Vibrat Res Southampton SO17 1BJ Hants England

This paper considers the applications of principal component analysis (PCA) for signal-based linear system identification. Linear time-invariant (LTI) single-input-single-output (SISO) and multi-input-multi-output (MIMO) system frequency response function (FRF) estimation problems are formulated on the basis of the eigen-value decomposition (EVD) of the input-output measurement spectral correlation matrix. It is demonstrated that resulting algorithms for the SISO and MIMO cases are equivalent to that of the maximum likelihood (ML) and the total least squares (TLS) approaches respectively. Originating from the proposed FRF estimation scheme, a moving-segment EVD procedure is developed for SISO time-varying transfer function estimation. Based on the sensitivity of the time-domain PCA to delays/shifts between signals, an extended lagged-covariance-matrix approach is introduced for delay detection from time series. (c) 2006 Elsevier Ltd. All rights reserved.

关键词： principal component analysis eigenvalue decomposition frequency response function estimation delay detection time-varying transfer function input-output covariance

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