Cancelable ECG Biometrics using Compressive Sensing-Generalized Likelihood Ratio Test

Electrocardiogram (ECG) has been investigated as promising biometrics, but it cannot be canceled and re-used once compromised just like other biometrics. We propose methods to overcome the issue of irrevocability in ECG biometrics without compromising performance. Our proposed cancelable user authentication uses a generalized likelihood ratio test (GLRT) based on a composite hypothesis testing in compressive sensing (CS) domain We also propose a permutation-based revocation method for CS-based cancelable biometrics so that it becomes resilient to record multiplicity attack. In addition, to compensate for inevitable performance degradation due to cancelable schemes, we also propose two performance improvement methods without undermining cancelable schemes: a self-guided ECG filtering and a T-wave shift model in our CS-GLRT. Finally, our proposed methods were evaluated for various cancelable biometrics criteria with the public ECG-ID data (89 subjects). Our cancelable ECG biometric methods yielded up to 93.0% detection probability at 2.0% false alarm ratio (PD*) and 3.8% equal error rate (EER), which are comparable to or even better than non-cancelable baseline with 93.2% PD* and 4.8% EER for challenging single pulse ECG authentication, respectively. Our proposed methods met all cancelable biometrics criteria theoretically or empirically. Our cancelable secure user template with our novel revocation process is practically non-invertible and robust to record multiplicity attack

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Department of Electrical EngineeringBiometrics such as fingerprint, iris, face, and electrocardiogram (ECG) have been investigated as convenient and powerful security tools that can potentially replace or supplement current possession or knowledge based authentication schemes. Recently, multi-spectral skin photomatrix (MSP) has been newly found as one of the biometrics. Moreover, since the interest of usage and security for wearable devices have been increasing, multi-modal biometrics authentication which is combining more than two modalities such as (iris + face) or (iris + fingerprint) for powerful and convenience authentication is widely proposed. However, one practical drawback of biometrics is irrevocability. Unlike password, biometrics can not be canceled and re-used once compromised since they are not changed forever. There have been several works on cancelable biometrics to overcome this drawback. ECG has been investigated as a promising biometrics, but there are few research on cancelable ECG biometrics. As we aim to study a way for multi-modal biometric scheme for wearable devices that is assumed circumstance under some limitations such as relatively high performance, low computing power, and limited information (not sharing users information to the public), in this study, we proposed a multi-modal biometrics authentication by combining ECG and MSP. For investigating the performances versus level of fusions, Adaboost algorithm was studied as a score level fusion method, and Majority Voting was studied as a decision level fusion method. Due to ECG signal is 1 dimensional, it provides benefits in wearable devices for overcoming the computing memory limitation. The reasons that we select MSP combination with ECG are it can be collected by measuring on inner-wrist of human body and it also can be considered as hardly stolen modality in remote ways. For proposed multi-modal biometrics, We evaluate our methods using collected data by Brain-Computer-Interface lab with 63 subjects. Our Adaboost based pro- posed multi modal biometrics method with performance boost yielded 99.7% detection probability at 0.1% false alarm ratio (PD0.1) and 0.3% equal error rate (EER), which are far better than simply combining by Majority Voting algorithm with 21.5% PD0.1 and 1.6% EER. Note that for training the Adaboost algorithm, we used only 9 people dataset which is assumed as public data and not included for testing data set, against for knowledge limitation as the other constraint. As initial step for user template protection, We proposed a cancelable ECG based user authentication using a composite hypothesis testing in compressive sensing do- main by deriving a generalized likelihood ratio test (GLRT) detector. We also pro- posed two performance boost tricks in compressive sensing domain to compensate for performance degradation due to cancelable schemes: user template guided filtering and T-wave shift model based GLRT detector for random projection domain. To verify our proposed method, we investigated cancelable biometrics criteria for the proposed methods to confirm that the proposed algorithms are indeed cancelable. For proposed cancelable ECG authentication, We evaluated our proposed methods using ECG data with 147 subjects from three public ECG data sets (ECG-ID, MIT- BIH Normal / Arrhythmia). Our proposed cancelable ECG authentication method is practically cancelable by satisfying all cancelable biometrics criteria. Moreover, our proposed method with performance boost tricks achieved 97.1% detection probability at 1% false alarm ratio (PD1) and 1.9% equal error rate (EER), which are even better than non-cancelable baseline with 94.4% PD1 and 3.1% EER for single pulse ECG authentication.ope

Multi-biometric templates using fingerprint and voice

As biometrics gains popularity, there is an increasing concern about privacy and misuse of biometric data held in central repositories. Furthermore, biometric verification systems face challenges arising from noise and intra-class variations. To tackle both problems, a multimodal biometric verification system combining fingerprint and voice modalities is proposed. The system combines the two modalities at the template level, using multibiometric templates. The fusion of fingerprint and voice data successfully diminishes privacy concerns by hiding the minutiae points from the fingerprint, among the artificial points generated by the features obtained from the spoken utterance of the speaker. Equal error rates are observed to be under 2% for the system where 600 utterances from 30 people have been processed and fused with a database of 400 fingerprints from 200 individuals. Accuracy is increased compared to the previous results for voice verification over the same speaker database

Towards cancelable multi-biometrics based on bloom filters: a case study on feature level fusion of face and iris

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. C. Rathgeb, M. Gomez-Barrero, C. Busch, J. Galbally, and J. Fierrez, "Towards cancelable multi-biometrics based on bloom filters: a case study on feature level fusion of face and iris", in International Workshop on Biometrics and Forensics (IWBF), 2015, p. 1-6In this work we propose a generic framework for generating an irreversible representation of multiple biometric templates based on adaptive Bloom filters. The presented technique enables a feature level fusion of different biometrics (face and iris) to a single protected template, improving privacy protection compared to the corresponding systems based on a single biometric trait. At the same time, a significant gain in biometric performance is achieved, confirming the sound- ness of the proposed technique.This work has been partially supported by projects Bio-Shield (TEC2012-34881) from Spanish MINECO, FIDELITY (FP7- SEC-284862) and BEAT (FP7-SEC-284989) from EU, the Center for Advanced Security Research Darmstadt (CASED) and C´atedra UAM-Telef´onica. Marta Gomez-Barrero is supported by a FPU Fellowship from Spanish MECD

Security and accuracy of fingerprint-based biometrics: A review

Biometric systems are increasingly replacing traditional password- and token-based authentication systems. Security and recognition accuracy are the two most important aspects to consider in designing a biometric system. In this paper, a comprehensive review is presented to shed light on the latest developments in the study of fingerprint-based biometrics covering these two aspects with a view to improving system security and recognition accuracy. Based on a thorough analysis and discussion, limitations of existing research work are outlined and suggestions for future work are provided. It is shown in the paper that researchers continue to face challenges in tackling the two most critical attacks to biometric systems, namely, attacks to the user interface and template databases. How to design proper countermeasures to thwart these attacks, thereby providing strong security and yet at the same time maintaining high recognition accuracy, is a hot research topic currently, as well as in the foreseeable future. Moreover, recognition accuracy under non-ideal conditions is more likely to be unsatisfactory and thus needs particular attention in biometric system design. Related challenges and current research trends are also outlined in this paper

Performance comparison of intrusion detection systems and application of machine learning to Snort system

This study investigates the performance of two open source intrusion detection systems (IDSs) namely Snort and Suricata for accurately detecting the malicious traffic on computer networks. Snort and Suricata were installed on two different but identical computers and the performance was evaluated at 10 Gbps network speed. It was noted that Suricata could process a higher speed of network traffic than Snort with lower packet drop rate but it consumed higher computational resources. Snort had higher detection accuracy and was thus selected for further experiments. It was observed that the Snort triggered a high rate of false positive alarms. To solve this problem a Snort adaptive plug-in was developed. To select the best performing algorithm for Snort adaptive plug-in, an empirical study was carried out with different learning algorithms and Support Vector Machine (SVM) was selected. A hybrid version of SVM and Fuzzy logic produced a better detection accuracy. But the best result was achieved using an optimised SVM with firefly algorithm with FPR (false positive rate) as 8.6% and FNR (false negative rate) as 2.2%, which is a good result. The novelty of this work is the performance comparison of two IDSs at 10 Gbps and the application of hybrid and optimised machine learning algorithms to Snort