53 research outputs found

    Process of Fingerprint Authentication using Cancelable Biohashed Template

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    Template protection using cancelable biometrics prevents data loss and hacking stored templates, by providing considerable privacy and security. Hashing and salting techniques are used to build resilient systems. Salted password method is employed to protect passwords against different types of attacks namely brute-force attack, dictionary attack, rainbow table attacks. Salting claims that random data can be added to input of hash function to ensure unique output. Hashing salts are speed bumps in an attacker’s road to breach user’s data. Research proposes a contemporary two factor authenticator called Biohashing. Biohashing procedure is implemented by recapitulated inner product over a pseudo random number generator key, as well as fingerprint features that are a network of minutiae. Cancelable template authentication used in fingerprint-based sales counter accelerates payment process. Fingerhash is code produced after applying biohashing on fingerprint. Fingerhash is a binary string procured by choosing individual bit of sign depending on a preset threshold. Experiment is carried using benchmark FVC 2002 DB1 dataset. Authentication accuracy is found to be nearly 97\%. Results compared with state-of art approaches finds promising

    A Secure Online Fingerprint Authentication System for Industrial IoT Devices over 5G Networks

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    The development of 5G networks has rapidly increased the use of Industrial Internet of Things (IIoT) devices for control, monitoring, and processing purposes. Biometric-based user authentication can prevent unauthorized access to IIoT devices, thereby safeguarding data security during production. However, most biometric authentication systems in the IIoT have no template protection, thus risking raw biometric data stored as templates in central databases or IIoT devices. Moreover, traditional biometric authentication faces slow, limited database holding capacity and data transmission problems. To address these issues, in this paper we propose a secure online fingerprint authentication system for IIoT devices over 5G networks. The core of the proposed system is the design of a cancelable fingerprint template, which protects original minutia features and provides privacy and security guarantee for both entity users and the message content transmitted between IIoT devices and the cloud server via 5G networks. Compared with state-of-the-art methods, the proposed authentication system shows competitive performance on six public fingerprint databases, while saving computational costs and achieving fast online matching

    Privacy-Preserving Biometric Authentication

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    Biometric-based authentication provides a highly accurate means of authentication without requiring the user to memorize or possess anything. However, there are three disadvantages to the use of biometrics in authentication; any compromise is permanent as it is impossible to revoke biometrics; there are significant privacy concerns with the loss of biometric data; and humans possess only a limited number of biometrics, which limits how many services can use or reuse the same form of authentication. As such, enhancing biometric template security is of significant research interest. One of the methodologies is called cancellable biometric template which applies an irreversible transformation on the features of the biometric sample and performs the matching in the transformed domain. Yet, this is itself susceptible to specific classes of attacks, including hill-climb, pre-image, and attacks via records multiplicity. This work has several outcomes and contributions to the knowledge of privacy-preserving biometric authentication. The first of these is a taxonomy structuring the current state-of-the-art and provisions for future research. The next of these is a multi-filter framework for developing a robust and secure cancellable biometric template, designed specifically for fingerprint biometrics. This framework is comprised of two modules, each of which is a separate cancellable fingerprint template that has its own matching and measures. The matching for this is based on multiple thresholds. Importantly, these methods show strong resistance to the above-mentioned attacks. Another of these outcomes is a method that achieves a stable performance and can be used to be embedded into a Zero-Knowledge-Proof protocol. In this novel method, a new strategy was proposed to improve the recognition error rates which is privacy-preserving in the untrusted environment. The results show promising performance when evaluated on current datasets

    Forensic analysis of large capacity digital storage devices

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    Digital forensic laboratories are failing to cope with the volume of digital evidence required to be analysed. The ever increasing capacity of digital storage devices only serves to compound the problem. In many law enforcement agencies a form of administrative triage takes place by simply dropping perceived low priority cases without reference to the data itself. Security agencies may also need days or weeks to analyse devices in order to detect and quantify encrypted data on the device.The current methodology often involves agencies creating a hash database of files where each known contraband file is hashed using a forensic hashing algorithm. Each file on a suspect device is similarly hashed and the hash compared against the contraband hash database. Accessing files via the file system in this way is a slow process. In addition deleted files or files on deleted or hidden partitions would not be found since their existence is not recorded in the file system.This thesis investigates the introduction of a system of triage whereby digital storage devices of arbitrary capacity can be quickly scanned to identify contraband and encrypted content with a high probability of detection with a known and controllable margin of error in a reasonable time. Such a system could classify devices as being worthy of further investigation or not and thus limit the number of devices being presented to digital forensic laboratories for examination.A system of triage is designed which bypasses the file system and uses the fundamental storage unit of digital storage devices, normally a 4 KiB block, rather than complete files. This allows fast sampling of the storage device. Samples can be chosen to give a controllable margin of error. In addition the sample is drawn from the whole address space of the device and so deleted files and partitions are also sampled. Since only a sample is being examined this is much faster than the traditional digital forensic analysis process.In order to achieve this, methods are devised that allow firstly the identification of 4 KiB blocks as belonging to a contraband file and secondly the classification of the block as encrypted or not. These methods minimise both memory and CPU loads so that the system may run on legacy equipment that may be in a suspect’s possession. A potential problem with the existence of blocks that are common to many files is quantified and a mitigation strategy developed.The system is tested using publically available corpora by seeding devices with contraband and measuring the detection rate during triage. Results from testing are positive, achieving a 99% probability of detecting 4 MiB of contraband on a 1 TB device within the time normally assigned for the interview of the device owner. Initial testing on live devices in a law enforcement environment has shown that sufficient evidence can be collected in under four minutes from a 1TB device to allow the equipment to be seized and the suspect to be charged.This research will lead to a significant reduction in the backlog of cases in digital forensic laboratories since it can be used for triage within the laboratory as well as at the scene of crime

    Signal processing and machine learning techniques for human verification based on finger textures

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    PhD ThesisIn recent years, Finger Textures (FTs) have attracted considerable attention as potential biometric characteristics. They can provide robust recognition performance as they have various human-speci c features, such as wrinkles and apparent lines distributed along the inner surface of all ngers. The main topic of this thesis is verifying people according to their unique FT patterns by exploiting signal processing and machine learning techniques. A Robust Finger Segmentation (RFS) method is rst proposed to isolate nger images from a hand area. It is able to detect the ngers as objects from a hand image. An e cient adaptive nger segmentation method is also suggested to address the problem of alignment variations in the hand image called the Adaptive and Robust Finger Segmentation (ARFS) method. A new Multi-scale Sobel Angles Local Binary Pattern (MSALBP) feature extraction method is proposed which combines the Sobel direction angles with the Multi-Scale Local Binary Pattern (MSLBP). Moreover, an enhanced method called the Enhanced Local Line Binary Pattern (ELLBP) is designed to e ciently analyse the FT patterns. As a result, a powerful human veri cation scheme based on nger Feature Level Fusion with a Probabilistic Neural Network (FLFPNN) is proposed. A multi-object fusion method, termed the Finger Contribution Fusion Neural Network (FCFNN), combines the contribution scores of the nger objects. The veri cation performances are examined in the case of missing FT areas. Consequently, to overcome nger regions which are poorly imaged a method is suggested to salvage missing FT elements by exploiting the information embedded within the trained Probabilistic Neural Network (PNN). Finally, a novel method to produce a Receiver Operating Characteristic (ROC) curve from a PNN is suggested. Furthermore, additional development to this method is applied to generate the ROC graph from the FCFNN. Three databases are employed for evaluation: The Hong Kong Polytechnic University Contact-free 3D/2D (PolyU3D2D), Indian Institute of Technology (IIT) Delhi and Spectral 460nm (S460) from the CASIA Multi-Spectral (CASIAMS) databases. Comparative simulation studies con rm the e ciency of the proposed methods for human veri cation. The main advantage of both segmentation approaches, the RFS and ARFS, is that they can collect all the FT features. The best results have been benchmarked for the ELLBP feature extraction with the FCFNN, where the best Equal Error Rate (EER) values for the three databases PolyU3D2D, IIT Delhi and CASIAMS (S460) have been achieved 0.11%, 1.35% and 0%, respectively. The proposed salvage approach for the missing feature elements has the capability to enhance the veri cation performance for the FLFPNN. Moreover, ROC graphs have been successively established from the PNN and FCFNN.the ministry of higher education and scientific research in Iraq (MOHESR); the Technical college of Mosul; the Iraqi Cultural Attach e; the active people in the MOHESR, who strongly supported Iraqi students

    The Partial Order Kernel and its Application to Understanding the Regulatory Grammar of Conserved Non-coding Elements

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    PhDConserved non-coding elements (CNEs) are regions of non-coding DNA which have remained evolutionarily conserved across various species over millions of years and are found to cluster near genes involved in early embryonic development, suggesting that they play an important role as regulatory elements. Indeed, many CNEs have been shown to act as enhancers; however, not all regulatory elements are conserved and in some cases, deletion of CNEs did not result in any notable phenotypes. These opposing ndings indicate that the functions of CNEs are still poorly understood and further research on these elements is needed to uncover the reasons for their extreme conservation. The aim of this thesis is to investigate the use and development of algorithms for decoding the regulatory grammar of CNEs. Initially, an assessment of several methods for functional classi cation of CNEs is provided. The results obtained using these methods are validated by functional assays and their limitations in capturing the grammar of CNEs are discussed. Motivated by these limitations, a partial order graph representation of the sequence of transcription factor binding sites (TFBSs) in a CNE that allows e cient handling of the overlapping sites is introduced. A dynamic programming-based method for aligning two such graphs and identifying regulatory signatures composed of co-occurring TFBSs is proposed and evaluated. The results demonstrate the predictive ability of this method, which can be used to prioritise regions for experimental validation. Building on this method, the partial order kernel (POKer) for comparison of strings containing alternative substrings and represented by partial order graphs is introduced. The POKer is evaluated in di erent sequence comparison tasks, including visual localisation. An approach using the POKer for functional classi cation of CNEs is introduced and its e ectiveness in capturing the grammar of CNEs is demonstrated. Finally, the implications of the results presented in this work for modelling the evolution of CNEs are discussed

    Handbook of Vascular Biometrics

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    Handbook of Vascular Biometrics

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    This open access handbook provides the first comprehensive overview of biometrics exploiting the shape of human blood vessels for biometric recognition, i.e. vascular biometrics, including finger vein recognition, hand/palm vein recognition, retina recognition, and sclera recognition. After an introductory chapter summarizing the state of the art in and availability of commercial systems and open datasets/open source software, individual chapters focus on specific aspects of one of the biometric modalities, including questions of usability, security, and privacy. The book features contributions from both academia and major industrial manufacturers
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