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نتیجه جستجو - Security

تعداد مقالات یافته شده: 1358
ردیف عنوان نوع
61 Adaptive Management of Multimodal Biometrics—A Deep Learning and Metaheuristic Approach
مدیریت تطبیقی بیومتریک چند حالته - یادگیری عمیق و رویکرد فرا مکاشفه ای-2021
This paper introduces the framework for adaptive rank-level biometric fusion: a new approach towards personal authentication. In this work, a novel attempt has been made to identify the optimal design parameters and framework of a multibiometric system, where the chosen biometric traits are subjected to rank-level fusion. Optimal fusion parameters depend upon the security level demanded by a particular biometric application. The proposed framework makes use of a metaheuristic approach towards adaptive fusion in the pursuit of achieving optimal fusion results at varying levels of security. Rank-level fusion rules have been employed to provide optimum performance by making use of Ant Colony Optimization technique. The novelty of the reported work also lies in the fact that the proposed design engages three biometric traits simultaneously for the first time in the domain of adaptive fusion, so as to test the efficacy of the system in selecting the optimal set of biometric traits from a given set. Literature reveals the unique biometric characteristics of the fingernail plate, which have been exploited in this work for the rigorous experimentation conducted. Index, middle and ring fingernail plates have been taken into consideration, and deep learning feature-sets of the three nail plates have been extracted using three customized pre-trained models, AlexNet, ResNet-18 and DenseNet-201. The adaptive multimodal performance of the three nail plates has also been checked using the already existing methods of adaptive fusion designed for addressing fusion at the score-level and decision- level. Exhaustive experiments have been conducted on the MATLAB R2019a platform using the Deep Learning Toolbox. When the cost of false acceptance is 1.9, experimental results obtained from the proposed framework give values of the average of the minimum weighted error rate as low as 0.0115, 0.0097 and 0.0101 for the AlexNet, ResNet-18 and DenseNet-201 based experiments respectively. Results demonstrate that the proposed system is capable of computing the optimal parameters for rank-level fusion for varying security levels, thus contributing towards optimal performance accuracy.© 2021 Elsevier B.V. All rights reserved.
Keywords: Adaptive Biometric Fusion | Ant Colony Optimization | Deep Learning | Fingernail Plate | Multimodal Biometrics | Rank-level Adaptive Fusion
مقاله انگلیسی
62 Trustworthy authorization method for security in Industrial Internet of Things
روش مجوز معتبر برای امنیت در اینترنت اشیا صنعتی-2021
Industrial Internet of Things (IIoT) realizes machine-to-machine communication and human–computer inter- action (HCI) through communication network, which makes industrial production automatic and intelligent. Security is critical in IIoT because of the interconnection of intelligent industrial equipment. In IIoT environment, legitimate human–computer interaction can only be performed by authorized professionals, and unauthorized access is not tolerated. In this paper, a reliable authentication method based on biological information is proposed. Specifically, the complete local binary pattern (CLPB) and the statistical local binary pattern (SLPB) are introduced to describe the local vein texture characteristics. Meanwhile, the contrast energy and frequency domain information are regarded as auxiliary information to interpret the finger vein. The distance between the features of the registration image and the test image is used to recognize the finger vein image, so as to realize identity authentication. The experiments are carried out on SDUMLA-FV database and FV-USM database, and results show that the presented method has achieved high recognition accuracy.
Keywords: Industrial Internet of Things (IIoT) | Human–computer interaction (HCI) | Biometric recognition | Comprehensive texture | Security system
مقاله انگلیسی
63 Human identification driven by deep CNN and transfer learning based on multiview feature representations of ECG
Human identification driven by deep CNN and transfer learning based on multiview feature representations of ECG-2021
Increasingly smart techniques for counterfeiting face and fingerprint traits have increased the potential threats to information security systems, creating a substantial demand for improved security and better privacy and identity protection. The internet of Things (IoT)-driven fingertip electrocardiogram (ECG) acquisition provides broad application prospects for ECG-based identity systems. This study focused on three major impediments to fingertip ECG: the impact of variations in acquisition status, the high computational complexity of traditional convolutional neural network (CNN) models and the feasibility of model migration, and a lack of sufficient fingertip samples. Our main contribution is a novel fingertip ECG identification system that integrates transfer learning and a deep CNN. The proposed system does not require manual feature extraction or suffer from complex model calculations, which improves its speed, and it is effective even when only a small set of training data exists. Using 1200 ECG recordings from 600 individuals, we consider 5 simulated yet potentially practical scenarios. When analyzing the overall training accuracy of the model, its mean accuracy for the 540 chest- collected ECG from PhysioNet exceeded 97.60 %, and for 60 subjects from the CYBHi fingertip-collected ECG, its mean accuracy reached 98.77 %. When simulating a real-world human recognition system on 5 public datasets, the validation accuracy of the proposed model can nearly reach 100 % recognition, outperforming the original GoogLeNet network by a maximum of 3.33 %. To some degree, the developed architecture provides a reference for practical applications of fingertip-collected ECG-based biometric systems and for information network security.
Keywords: Off-the-person | Fingertip ECG biometric | Human identification | Convolutional neural network (CNN) | Transfer learning
مقاله انگلیسی
64 A novel multi-lead ECG personal recognition based on signals functional and structural dependencies using time-frequency representation and evolutionary morphological CNN
تشخیص شخصی نوار قلب ECG مبتنی بر وابستگی های عملکردی و ساختاری سیگنالها با استفاده از نمایش فرکانس زمان و CNN مورفولوژیکی تکاملی-2021
Biometric recognition systems have been employed in many aspects of life such as security technologies, data protection, and remote access. Physiological signals, e.g. electrocardiogram (ECG), can potentially be used in biometric recognition. From a medical standpoint, ECG leads have structural and functional dependencies. In fact, precordial ECG leads view the heart from different axial angles, whereas limb leads view it from various coronal angles. This study aimed to design a personal biometric recognition system based on ECG signals by estimating these latent medical variables. To estimate functional dependencies, within-correlation and cross- correlation in time-frequency domain between ECG leads were calculated and represented in the form of extended adjacency matrices. CNN trees were then introduced through genetic programming for the automated estimation of structural dependencies in extended adjacency matrices. CNN trees perform the deep feature learning process by using structural morphology operators. The proposed system was designed for both closed-set identification and verification. It was then tested on two datasets, i.e. PTB and CYBHi, for performance evaluation. Compared with the state-of-the-art methods, the proposed method outperformed all of them.
Keywords: Biometrics | Electrocardiogram | Functional dependencies | Structural dependencies | Genetic programming | Convolutional neural networks
مقاله انگلیسی
65 Digital Livestock Farming
دامداری دیجیتال-2021
As the global human population increases, livestock agriculture must adapt to provide more livestock products and with improved efficiency while also addressing concerns about animal welfare, environmental sustainability, and public health. The purpose of this paper is to critically review the current state of the art in digitalizing animal agriculture with Precision Livestock Farming (PLF) technologies, specifically biometric sensors, big data, and blockchain technology. Biometric sensors include either noninvasive or invasive sensors that monitor an individual animal’s health and behavior in real time, allowing farmers to integrate this data for population-level analyses. Real-time information from biometric sensors is processed and integrated using big data analytics systems that rely on statistical algorithms to sort through large, complex data sets to provide farmers with relevant trending patterns and decision-making tools. Sensors enabled blockchain technology affords secure and guaranteed traceability of animal products from farm to table, a key advantage in monitoring disease outbreaks and preventing related economic losses and food-related health pandemics. Thanks to PLF technologies, livestock agriculture has the potential to address the abovementioned pressing concerns by becoming more transparent and fostering increased consumer trust. However, new PLF technologies are still evolving and core component technologies (such as blockchain) are still in their infancy and insufficiently validated at scale. The next generation of PLF technologies calls for preventive and predictive analytics platforms that can sort through massive amounts of data while accounting for specific variables accurately and accessibly. Issues with data privacy, security, and integration need to be addressed before the deployment of multi-farm shared PLF solutions be- comes commercially feasible. Implications Advanced digitalization technologies can help modern farms optimize economic contribution per animal, reduce the drudgery of repetitive farming tasks, and overcome less effective isolated solutions. There is now a strong cultural emphasis on reducing animal experiments and physical contact with animals in-order-to enhance animal welfare and avoid disease outbreaks. This trend has the potential to fuel more research on the use of novel biometric sensors, big data, and blockchain technology for the mutual benefit of livestock producers, consumers, and the farm animals themselves. Farmers’ autonomy and data-driven farming approaches compared to experience-driven animal manage- ment practices are just several of the multiple barriers that digitalization must overcome before it can become widely implemented.
Keywords: Precision Livestock Farming | digitalization | Digital Technologies in Livestock Systems | sensor technology | big data | blockchain | data models | livestock agriculture
مقاله انگلیسی
66 Open code biometric tap pad for smartphones
باز کردن کد ضربه گیر بیومتریک برای تلفن های هوشمند-2021
Poor security practices among smartphone users, such as the use of simple, easily guessed passcodes for logins, are a result of the effort required to memorize stronger ones. In this paper, we devise a concept of ‘‘open code’’ biometric tap pad to authenticate smartphone users, which eliminates the need of memorizing secret codes. A biometric tap pad consists of a grid of buttons each labeled with a unique digit. The user attempting to log into the phone will tap these buttons in a given sequence. He/she will not memorize this tap sequence. Instead, the sequence will be displayed on the screen. The focus here is how the user types the sequence. This typing behavior is used for authentication. An open code biometric tap pad has several advantages, such as(1) users do not need to memorize passcodes, (2) manufacturers do not need to include extra sensors, and (3) onlookers have no chance to practice shoulder-surfing. We designed three tap pads and incorporated them into an Android app. We evaluated the performance of these tap pads by experimenting with three sequence styles and five different fingers: two thumbs, two index fingers, and the ‘‘usual’’ finger. We collected data from 33 participants over two weeks. We tested three machine learning algorithms: Support Vector Machine, Artificial Neural Network, and Random Forest. Experimental results show significant promise of open code biometric tap pads as a solution to the problem of weak smartphone security practices used by a large segment of the population.
Keywords: Smartphone security | Behavioral biometrics | Touchscreen behavior | Open code | Biometric tap pad
مقاله انگلیسی
67 A cancelable biometric authentication system based on feature-adaptive random projection
یک سیستم احراز هویت بیومتریک قابل لغو بر اساس طرح تصادفی سازگار با ویژگی-2021
Biometric template data protection is critical in preventing user privacy and identity from leakage. Random projection based cancelable biometrics is an efficient and effective technique to achieve biometric template protection. However, traditional random projection based cancelable template design suffers from the attack via record multiplicity (ARM), where an adversary obtains multiple transformed templates from different applica- tions and the associated parameter keys so as to assemble them into a full-rank linear equation system, thereby retrieving the original feature vector. To address this issue, in this paper we propose a feature-adaptive random projection based method, in which the projection matrixes, the key to the ARM, are generated from one basic matrix in conjunction with local feature slots. The generated projection matrixes are discarded after use, thus making it difficult for the adversary to launch the ARM. Moreover, the random projection in the proposed method is performed on a local-feature basis. This feature-adaptive random projection can mitigate the negative impact of biometric uncertainty on recognition accuracy, as it limits the error to part of the transformed feature vector rather than the entire vector. The proposed method is evaluated on four public available databases FVC2002 DB1-DB3 and FVC2004 DB2. The experimental results and security analysis show the validity of the proposed method.
Keywords: Biometric authentication | Template protection | Random projection | Cancelable biometrics
مقاله انگلیسی
68 Wood supply chain risks and risk mitigation strategies: A systematic review focusing on the Northern hemisphere
خطرات زنجیره تامین چوب و استراتژی های کاهش خطر: یک مرور سیستماتیک با تمرکز بر نیمکره شمالی-2021
This paper presents a systematic literature review on both the risks affecting wood supply security and risk mitigation strategies by quantitative and qualitative data analysis. It describes wood-specific supply chain risks, thereupon resulting impacts and counteracting strategies to ensure supply. Risks, impacts, and strategies are documented as basis for a comparative analysis, discussion of results, challenges and research gaps. Finally, the suitability and the limitations of the chosen methodology and the achieved results are discussed. Scanning wood supply chain risks and supply strategies, most of the reviewed papers focus on wood supply for bioenergy generation and only a few studies investigate wood supply chain risk issues for the sawing, wood panel, pulp and paper industries, or biorefineries.This review differs significantly from other reviews in this field as it considers the entire wood value chain including recent studies on new chemical wood-based products and thus provides a more complete picture of the wood-based bioeconomy. Consequently, it contributes to the literature by providing an overarching investigation of the risks affecting wood supply security and possible side effects of a growing wood-based bioeconomy. It was found that comprehensive value chain analyses considering established wood products, large-volume bioenergy products, as well as established and new chemical wood-based products in the context of wood supply security are missing. Studies that map the entire wood value chain with its multilevel interdependences and integrating cascading use of wood are lacking.
Keywords: Wood supply | Wood supply chain risk | Supply risk mitigation | Wood supply strategy | Wood-based bioeconomy
مقاله انگلیسی
69 Deep belief network-based hybrid model for multimodal biometric system for futuristic security applications
مدل ترکیبی مبتنی بر باور عمیق برای سیستم بیومتریک چند حالته برای برنامه های امنیتی آینده-2021
Biometrics is the technology to identify humans uniquely based on face, iris, and fingerprints, etc. Biometric authentication allows the person recognition automatically on the basis of behavioral or physiological charac- teristics. Biometrics are broadly employed in several commercial as well as the official identification systems for automatic access control. This paper introduces the model for multimodal biometric recognition based on score level fusion method. The overall procedure of the proposed method involves five steps, such as pre-processing, feature extraction, recognition score using Multi- support vector neural network (Multi-SVNN) for all traits, score level fusion, and recognition using deep belief neural network (DBN). The first step is to input the training images into pre-processing steps. Thus, the pre-processing of three traits, like iris, ear, and finger vein is done. Then, the feature extraction is done for each modality to extract the features. After that, the texture features are extracted from pre-processed images of the ear, iris, and finger vein, and the BiComp features are acquired from individual images using a BiComp mask. Then, the recognition score is computed based on the Multi-SVNN classifier to provide the score individually for all three traits, and the three scores are provided to the DBN. The DBN is trained using the chicken earthworm optimization algorithm (CEWA). The CEWA is the integration of the chicken swarm optimization (CSO), and earthworm optimization algorithm (EWA) for the optimal authentication of the person. The analysis proves that the developed method acquired a maximal accuracy of 95.36%, maximal sensitivity of 95.85%, and specificity of 98.79%, respectively.
Keywords: Multi-modal Bio-metric system | Chicken Swarm Optimization | Earthworm Optimization algorithm | Deep Belief Network | Multi-SVNN
مقاله انگلیسی
70 Provably secure biometric-based client–server secure communication over unreliable networks
ارتباطات امن مشتری و سرور مبتنی بر بیومتریک به طور قابل اطمینان در شبکه های غیرقابل اعتماد-2021
Due to rapid advancement in internet technologies, remote client access has become much more comfortable than in previous days. The responsibility of the system does not limit even after providing access to resources. In every system, there is a possibility that an adversary may use the resources of the system without prior authentication. This will create interruption, and the clients of the system will ultimately be affected, so the system must provide some authentication mechanism. Therefore, an authentication scheme needs to be introduced, which provides security against different attacks. Consequently, in this article, we propose a reliable and secure three factor authentication scheme that prevents various security attacks. The provable security of the proposed scheme is proved through a widely used random oracle model. We implemented the cryptographic operations of proposed and related schemes on a desktop system having good specifications to get the experimental results. Moreover, the presented scheme is compared with the existing schemes to compare its performance. The performance evaluation shows that our scheme is lightweight and efficient in terms of computation and communication costs as compared to related competing schemes.
Keywords: Anonymity | Authentication | Impersonation attack | Session key | Smartcard
مقاله انگلیسی
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