The Experts below are selected from a list of 14358 Experts worldwide ranked by ideXlab platform
Michael A. Temple - One of the best experts on this subject based on the ideXlab platform.
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Feature Selection for RF Fingerprinting with Multiple Discriminant Analysis and Using ZigBee Device Emissions
2016Co-Authors: Trevor J. Bihl, Kenneth W. Bauer, Michael A. TempleAbstract:The proliferation of low-cost IEEE 802.15.4 ZigBee wireless devices in critical infrastructure applications presents security challenges. Network security commonly relies on bit-level credentials that are easily replicated and exploited by hackers. Unauthorized access can be mitigated by physical layer (PHY) security measures that exploit device-dependent emission characteristics that are sufficiently unique to discriminate devices. RF distinct native attribute (RF-DNA) fingerprinting is a PHY-based security measure, which computes statistical features extracted from such device emissions. However, the RF-DNA fingerprints can be numerous, correlated, and noisy, therefore, a dimensional reduction Analysis (DRA) via feature selection is, therefore, of interest. Device classification with DRA feature subsets is evaluated using a Multiple Discriminant Analysis (MDA) classifier. Determining feature relevance from MDA was generally dismissed in prior RF fingerprinting work and is seldom considered in other applications. Here, the MDA feature relevance is revisited using a proposed eigen-based MDA loadings fusion (MLF) methodology. The MDA classification models are adopted and used to assess device identification (ID) classification and verification performance for both the authorized and unauthorized (rogue) devices using a claimed versus actual biometric methodology. Performance is compared for six DRA methods using: 1) a two-sample Kolmogorov-Smirnov test; 2) one-way Analysis of variance F-test statistics; 3) a Wilk's lambda ratio; 4) generalized relevance learning vector quantized-improved relevance; 5) randomly selected; and 6) the proposed MLF method. Quantitative and qualitative dimensionality assessment methods are compared and contrasted to establish upper bounds on the number of retained features. Experimentally collected ZigBee emissions are considered and ZigBee device classification and ID verification performance using DRA subsets are compared with a full-dimensional feature set. Results show that DRA via the proposed MLF method is superior and more robust than competing methods.
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Intrinsic Physical-Layer Authentication of Integrated Circuits
2012Co-Authors: William E. Cobb, Howard J Patton, Rusty O. Baldwin, Michael A. Temple, Yong C. KimAbstract:Radio-frequency distinct native attribute (RF-DNA) fingerprinting is adapted as a physical-layer technique to improve the security of integrated circuit (IC)-based multifactor authentication systems. Device recognition tasks (both identification and verification) are accomplished by passively monitoring and exploiting the intrinsic features of an IC's unintentional RF emissions without requiring any modification to the device being analyzed. Device discrimination is achieved using RF-DNA fingerprints comprised of higher order statistical features based on instantaneous amplitude, phase, and frequency responses as a device executes a sequence of operations. The recognition system is trained using Multiple Discriminant Analysis to reduce data dimensionality while retaining class separability, and the resultant fingerprints are classified using a linear Bayesian classifier. Demonstrated identification and verification performance includes average identification accuracy of greater than 99.5% and equal error rates of less than 0.05% for 40 near-identical devices. Depending on the level of required classification accuracy, RF-DNA fingerprint-based authentication is well-suited for implementation as a countermeasure to device cloning, and is promising for use in a wide variety of related security problems.
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Physical layer identification of embedded devices using RF-DNA fingerprinting
2010Co-Authors: William E. Cobb, Eric W. Garcia, Rusty O. Baldwin, Michael A. Temple, Yong C. KimAbstract:RF distinct native attribute (RF-DNA) fingerprinting is introduced as a means to uniquely identify embedded processors and other integrated circuit devices by passively monitoring and exploiting unintentional RF emissions. Device discrimination is accomplished using RF-DNA fingerprints comprised of higher-order statistical features based on instantaneous amplitude and frequency responses as a device executes a sequence of operations. The resultant fingerprints are input to a Multiple Discriminant Analysis/Maximum Likelihood (MDA/ML) processor for subsequent device discrimination. Using devices from a given manufacturer and experimentally collected side channel signals, 90–100% identification accuracy is achieved for SNR ≥ 12 dB for devices with identical part numbers from the same production lot. Depending on the level of required classification accuracy, RF-DNA fingerprinting is well-suited for realistic environments and practical operating distances. Applications of device RF-DNA fingerprints include supplementary physical layer authentication of secure tokens (e.g. smart cards), detection of counterfeit electronic devices or unauthorized modification, and forensic attribution of a device's unique identity in criminal or other investigations.
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sensitivity Analysis of burst detection and rf fingerprinting classification performance
2009Co-Authors: Randall W Klein, Michael A. Temple, Michael J Mendenhall, Donald R ReisingAbstract:There has been a recent shift toward improving wireless access security within the OSI PHY layer by exploiting RF features that are inherently device specific and difficult to replicate by an unintended party. This work addresses the extraction and exploitation of RF "fingerprints" to classify emissions and provide device-specific identification. Burst transient detection precedes RF fingerprint extraction and is generally the most critical step in the overall process. This work provides a much needed sensitivity Analysis of burst detection capability. The Analysis is conducted using instantaneous amplitude responses with both Fractal-Bayesian Step Change Detection (Fractal-BSCD) and Variance Trajectory (VT) processes. The performance of each method is evaluated under varying SNR conditions using experimentally collected 802.11a OFDM signals. The impact of transient detection error on signal classification performance is then demonstrated using RF fingerprints and Multiple Discriminant Analysis (MDA) with Maximum Likelihood (ML) classification. The VT technique emerges as the better alternative for all SNRs considered and yields MDA-ML classification accuracy that is consistent with "perfect" transient estimation performance.
Youngchan Lee - One of the best experts on this subject based on the ideXlab platform.
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application of support vector machines to corporate credit rating prediction
2007Co-Authors: Youngchan LeeAbstract:Corporate credit rating Analysis has drawn a lot of research interests in previous studies, and recent studies have shown that machine learning techniques achieved better performance than traditional statistical ones. This paper applies support vector machines (SVMs) to the corporate credit rating problem in an attempt to suggest a new model with better explanatory power and stability. To serve this purpose, the researcher uses a grid-search technique using 5-fold cross-validation to find out the optimal parameter values of RBF kernel function of SVM. In addition, to evaluate the prediction accuracy of SVM, the researcher compares its performance with those of Multiple Discriminant Analysis (MDA), case-based reasoning (CBR), and three-layer fully connected back-propagation neural networks (BPNs). The experiment results show that SVM outperforms the other methods.
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bankruptcy prediction using support vector machine with optimal choice of kernel function parameters
2005Co-Authors: Jae H Min, Youngchan LeeAbstract:Bankruptcy prediction has drawn a lot of research interests in previous literature, and recent studies have shown that machine learning techniques achieved better performance than traditional statistical ones. This paper applies support vector machines (SVMs) to the bankruptcy prediction problem in an attempt to suggest a new model with better explanatory power and stability. To serve this purpose, we use a grid-search technique using 5-fold cross-validation to find out the optimal parameter values of kernel function of SVM. In addition, to evaluate the prediction accuracy of SVM, we compare its performance with those of Multiple Discriminant Analysis (MDA), logistic regression Analysis (Logit), and three-layer fully connected back-propagation neural networks (BPNs). The experiment results show that SVM outperforms the other methods.
M Tico - One of the best experts on this subject based on the ideXlab platform.
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fingerprint classification based on Multiple Discriminant Analysis
2002Co-Authors: J Malinen, V Onnia, M TicoAbstract:In this paper an effective fingerprint classification method based on Multiple Discriminant Analysis (MDA) is presented. The typology information is used to classify fingerprints. We also describe a feature extraction method based on Gabor filters. The effectiveness of our method is based on matters, that a reference point is searched from the region of interest and after finding the reference point, we crop the smaller image below the reference point. Then every operation, like feature calculation, is done only to this smaller image. The method was tested using artificially generated fingerprint database. With a uniform fingerprint distribution our classifier works an accuracy of 95.8% for the five-class problem.
J Malinen - One of the best experts on this subject based on the ideXlab platform.
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fingerprint classification based on Multiple Discriminant Analysis
2002Co-Authors: J Malinen, V Onnia, M TicoAbstract:In this paper an effective fingerprint classification method based on Multiple Discriminant Analysis (MDA) is presented. The typology information is used to classify fingerprints. We also describe a feature extraction method based on Gabor filters. The effectiveness of our method is based on matters, that a reference point is searched from the region of interest and after finding the reference point, we crop the smaller image below the reference point. Then every operation, like feature calculation, is done only to this smaller image. The method was tested using artificially generated fingerprint database. With a uniform fingerprint distribution our classifier works an accuracy of 95.8% for the five-class problem.
Yong C. Kim - One of the best experts on this subject based on the ideXlab platform.
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Intrinsic Physical-Layer Authentication of Integrated Circuits
2012Co-Authors: William E. Cobb, Howard J Patton, Rusty O. Baldwin, Michael A. Temple, Yong C. KimAbstract:Radio-frequency distinct native attribute (RF-DNA) fingerprinting is adapted as a physical-layer technique to improve the security of integrated circuit (IC)-based multifactor authentication systems. Device recognition tasks (both identification and verification) are accomplished by passively monitoring and exploiting the intrinsic features of an IC's unintentional RF emissions without requiring any modification to the device being analyzed. Device discrimination is achieved using RF-DNA fingerprints comprised of higher order statistical features based on instantaneous amplitude, phase, and frequency responses as a device executes a sequence of operations. The recognition system is trained using Multiple Discriminant Analysis to reduce data dimensionality while retaining class separability, and the resultant fingerprints are classified using a linear Bayesian classifier. Demonstrated identification and verification performance includes average identification accuracy of greater than 99.5% and equal error rates of less than 0.05% for 40 near-identical devices. Depending on the level of required classification accuracy, RF-DNA fingerprint-based authentication is well-suited for implementation as a countermeasure to device cloning, and is promising for use in a wide variety of related security problems.
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Physical layer identification of embedded devices using RF-DNA fingerprinting
2010Co-Authors: William E. Cobb, Eric W. Garcia, Rusty O. Baldwin, Michael A. Temple, Yong C. KimAbstract:RF distinct native attribute (RF-DNA) fingerprinting is introduced as a means to uniquely identify embedded processors and other integrated circuit devices by passively monitoring and exploiting unintentional RF emissions. Device discrimination is accomplished using RF-DNA fingerprints comprised of higher-order statistical features based on instantaneous amplitude and frequency responses as a device executes a sequence of operations. The resultant fingerprints are input to a Multiple Discriminant Analysis/Maximum Likelihood (MDA/ML) processor for subsequent device discrimination. Using devices from a given manufacturer and experimentally collected side channel signals, 90–100% identification accuracy is achieved for SNR ≥ 12 dB for devices with identical part numbers from the same production lot. Depending on the level of required classification accuracy, RF-DNA fingerprinting is well-suited for realistic environments and practical operating distances. Applications of device RF-DNA fingerprints include supplementary physical layer authentication of secure tokens (e.g. smart cards), detection of counterfeit electronic devices or unauthorized modification, and forensic attribution of a device's unique identity in criminal or other investigations.
