The Experts below are selected from a list of 12921 Experts worldwide ranked by ideXlab platform
Anthony Skjellum - One of the best experts on this subject based on the ideXlab platform.
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Radio Identity Verification-based IoT Security Using RF-DNA Fingerprints and SVM
IEEE Internet of Things Journal, 2024Co-Authors: Donald Reising, Joseph Cancelleri, Daniel T. Loveless, Farah Kandah, Anthony SkjellumAbstract:It is estimated that the number of Internet of Things (IoT) devices will reach 75 billion in the next five years. Most of those currently and soon-to-be deployed devices lack sufficient security to protect themselves and their networks from attacks by malicious IoT devices masquerading as authorized devices in order to circumvent digital authentication approaches. This work presents a Physical (PHY) layer IoT authentication approach capable of addressing this critical security need through the use of feature-reduced, Radio Frequency-Distinct Native Attributes (RF-DNA) fingerprints and Support Vector Machines (SVM). This work successfully demonstrates (i) authorized Identity (ID) Verification across three trials of six randomly chosen radios at signal-to-noise ratios greater than or equal to 6 dB and (ii) rejection of all rogue radio ID spoofing attacks at signal-to-noise ratios greater than or equal to 3 dB using RF-DNA fingerprints whose features are selected using the Relief-F algorithm.
Fan Yang - One of the best experts on this subject based on the ideXlab platform.
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information fusion of biometrics based on fingerprint hand geometry and palm print
2007 IEEE Workshop on Automatic Identification Advanced Technologies, 2007Co-Authors: Fan Yang, Qun Xia Wang, Didi YaoAbstract:Since unimodal biometric systems based on single source of biometric information are always affected by problems such as noisy sensor data, no-universality and susceptibility to circumvention, using multimodal biometric systems that consolidate evidence from multiple biometric sources can remove some of these problems. In this paper, fingerprint, palm-print and hand-geometry are combined for person Identity Verification. Unlike other multimodal biometric systems, the user does not have to undergo the inconvenience of using two different sensors as three biometrics can be taken from the same image. Wavelet transform is employed to extract the features from fingerprint and palm-print and hand-geometry feature (such as width, length) is also extracted after the preprocessing phase. We employ feature fusion and mach score fusion together to establish Identity. The system was tested on a database of 98 persons. The test performance results indicate the feasibility of the combination.
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A New Mixed-Mode Biometrics Information Fusion Based-on Fingerprint, Hand-geometry and Palm-print
Fourth International Conference on Image and Graphics (ICIG 2007), 2007Co-Authors: Fan YangAbstract:Using multimodal biometric systems that consolidate evidence from multiple biometric sources can remove such as noisy sensor data, no-universality problems. In this paper, fingerprint, palm-print and hand-geometry are combined for person Identity Verification. Unlike other multimodal biometric systems, three biometrics can be taken from the same image. Wavelet transform to extract the features from fingerprint and palm-print is used and hand-geometry feature (such as width and length) is extracted after the pre-processing phase. We employ feature fusion and much score fusion together to establish Identity. The system was tested on a database of 98 persons. The test performance results indicate the possibility of the combination.
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implementation of an rbf neural network on embedded systems real time face tracking and Identity Verification
IEEE Transactions on Neural Networks, 2003Co-Authors: Fan Yang, Michel PaindavoineAbstract:This paper describes a real time vision system that allows us to localize faces in video sequences and verify their Identity. These processes are image processing techniques based on the radial basis function (RBF) neural network approach. The robustness of this system has been evaluated quantitatively on eight video sequences. We have adapted our model for an application of face recognition using the Olivetti Research Laboratory (ORL), Cambridge, UK, database so as to compare the performance against other systems. We also describe three hardware implementations of our model on embedded systems based on the field programmable gate array (FPGA), zero instruction set computer (ZISC) chips, and digital signal processor (DSP) TMS320C62, respectively. We analyze the algorithm complexity and present results of hardware implementations in terms of the resources used and processing speed. The success rates of face tracking and Identity Verification are 92% (FPGA), 85% (ZISC), and 98.2% (DSP), respectively. For the three embedded systems, the processing speeds for images size of 288 /spl times/ 352 are 14 images/s, 25 images/s, and 4.8 images/s, respectively.
Sungzoon Cho - One of the best experts on this subject based on the ideXlab platform.
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keystroke dynamics Identity Verification its problems and practical solutions
Computers & Security, 2004Co-Authors: Sungzoon ChoAbstract:Password is the most widely used Identity Verification method in computer security domain. However, because of its simplicity, it is vulnerable to imposter attacks. Use of keystroke dynamics can result in a more secure Verification system. Recently, Cho et al. (J Organ Comput Electron Commerce 10 (2000) 295) proposed autoassociative neural network approach, which used only the user's typing patterns, yet reporting a low error rate: 1.0% false rejection rate (FRR) and 0% false acceptance rate (FAR). However, the previous research had some limitations: (1) it took too long to train the model; (2) data were preprocessed subjectively by a human; and (3) a large data set was required. In this article, we propose the corresponding solutions for these limitations with an SVM novelty detector, GA-SVM wrapper feature subset selection, and an ensemble creation based on feature selection, respectively. Experimental results show that the proposed methods are promising, and that the keystroke dynamics is a viable and practical way to add more security to Identity Verification.
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ga svm wrapper approach for feature subset selection in keystroke dynamics Identity Verification
International Joint Conference on Neural Network, 2003Co-Authors: Sungzoon ChoAbstract:Password is the most widely used Identity Verification method in computer security domain. However, due to its simplicity, it is vulnerable to imposter attacks. Keystroke dynamics adds a shield to password. Password typing patterns or timing vectors of a user are measured and used to train a novelty detector model. However, without manual pre-processing to remove noises and outliers resulting from typing inconsistencies, a poor detection accuracy results. Thus, in this paper, we propose an automatic feature subset selection process that can automatically selects a relevant subset of features and ignores the rest, thus producing a better accuracy. Genetic algorithm is employed to implement a randomized search and SVM, an excellent novelty detector with fast learning speed, is employed as a base learner. Preliminary experiments show a promising result.
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web based keystroke dynamics Identity Verification using neural network
Journal of Organizational Computing and Electronic Commerce, 2000Co-Authors: Sungzoon Cho, Chigeun Han, Dae Hee Han, Hyungil KimAbstract:Password typing is the most widely used Identity Verification method in Web based electronic commerce. Due to its simplicity, however, it is vulnerable to imposter attacks. Keystroke dynamics and password checking can be combined to result in a more secure Verification system. We propose an autoassociator neural network that is trained with the timing vectors of the owner's keystroke dynamics and then used to discriminate between the owner and an imposter. An imposter typing the correct password can be detected with very high accuracy using the proposed approach. This approach can be effectively implemented by a Java applet and used for the Web.
Donald Reising - One of the best experts on this subject based on the ideXlab platform.
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Radio Identity Verification-based IoT Security Using RF-DNA Fingerprints and SVM
IEEE Internet of Things Journal, 2024Co-Authors: Donald Reising, Joseph Cancelleri, Daniel T. Loveless, Farah Kandah, Anthony SkjellumAbstract:It is estimated that the number of Internet of Things (IoT) devices will reach 75 billion in the next five years. Most of those currently and soon-to-be deployed devices lack sufficient security to protect themselves and their networks from attacks by malicious IoT devices masquerading as authorized devices in order to circumvent digital authentication approaches. This work presents a Physical (PHY) layer IoT authentication approach capable of addressing this critical security need through the use of feature-reduced, Radio Frequency-Distinct Native Attributes (RF-DNA) fingerprints and Support Vector Machines (SVM). This work successfully demonstrates (i) authorized Identity (ID) Verification across three trials of six randomly chosen radios at signal-to-noise ratios greater than or equal to 6 dB and (ii) rejection of all rogue radio ID spoofing attacks at signal-to-noise ratios greater than or equal to 3 dB using RF-DNA fingerprints whose features are selected using the Relief-F algorithm.
Elisa Bertino - One of the best experts on this subject based on the ideXlab platform.
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multifactor Identity Verification using aggregated proof of knowledge
Systems Man and Cybernetics, 2010Co-Authors: Abhilasha Bhargavspantzel, Anna Cinzia Squicciarini, Rui Xue, Elisa BertinoAbstract:The problem of Identity theft, that is, the act of impersonating others' identities by presenting stolen identifiers or proofs of identities, has been receiving increasing attention because of its high financial and social costs. In this paper, we address the problem of Verification of such identifiers and proofs of Identity. Our approach is based on the concept of privacy preserving multifactor Verification of such identifiers and proofs achieved by the development of a new cryptographic primitive, which uses aggregate signatures on commitments that are then used for aggregate zero-knowledge proof of knowledge (ZKPK) protocols. The resultant signatures are very short and the ZKPs are succinct and efficient. We prove the security of our scheme under the co-gap Diffie-Hellman (co-GDH) assumption for groups with bilinear maps. Our cryptographic scheme is an improvement in terms of the performance, flexibility, and storage requirements than the existing efficient ZKPK techniques that may be used to prove under zero knowledge and the knowledge of multiple secrets.
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biometrics based identifiers for digital Identity management
Identity and Trust on the Internet, 2010Co-Authors: Abhilasha Bhargavspantzel, Elisa Bertino, Anna Cinzia Squicciarini, Xiangwei Kong, Weike ZhangAbstract:We present algorithms to reliably generate biometric identifiers from a user's biometric image which in turn is used for Identity Verification possibly in conjunction with cryptographic keys. The biometric identifier generation algorithms employ image hashing functions using singular value decomposition and support vector classification techniques. Our algorithms capture generic biometric features that ensure unique and repeatable biometric identifiers. We provide an empirical evaluation of our techniques using 2569 images of 488 different individuals for three types of biometric images; namely fingerprint, iris and face. Based on the biometric type and the classification models, as a result of the empirical evaluation we can generate biometric identifiers ranging from 64 bits up to 214 bits. We provide an example use of the biometric identifiers in privacy preserving multi-factor Identity Verification based on zero knowledge proofs. Therefore several Identity Verification factors, including various traditional Identity attributes, can be used in conjunction with one or more biometrics of the individual to provide strong Identity Verification. We also ensure security and privacy of the biometric data. More specifically, we analyze several attack scenarios. We assure privacy of the biometric using the one-way hashing property, in that no information about the original biometric image is revealed from the biometric identifier.
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privacy preserving digital Identity management for cloud computing
IEEE Data(base) Engineering Bulletin, 2009Co-Authors: Elisa Bertino, Federica Paci, Rodolfo Ferrini, Ning ShangAbstract:Digital Identity management services are crucial in cloud computing infrastructures to authenticate users and to support flexible access control to services, based on user Identity properties (also called attributes) and past interaction histories. Such services should preserve the privacy of users, while at the same time enhancing interoperability across multiple domains and simplifying management of Identity Verification. In this paper we propose an approach addressing such requirements, based on the use of high-level Identity Verification policies expressed in terms of Identity attributes, zero-knolwedge proof protocols, and semantic matching techniques. The paper describes the basic techniques we adopt and the architeture of a system developed based on these techniques, and reports performance experimental
