Security and Cryptographic Challenges for Authentication Based on Biometrics Data

Nita, Stefania Loredana; Mihăilescu, Marius Iulian; Pau, Valentin

doi:10.20944/preprints201810.0618.v1

Cited by 12 publications

(4 citation statements)

References 4 publications

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“…However, although it is not considered very secure [ 8 ], RC4 continues to motivate research nowadays [ 8 , 9 , 10 ]. Furthermore, this cipher is a good option to measure the effectiveness of methods that analyze weaknesses in stream ciphers related to those already known in RC4 [ 11 , 12 , 13 , 14 ], or to check the performance of hardware or software schemes that make use of cryptography [ 15 , 16 , 17 ].…”

Section: Preliminariesmentioning

confidence: 99%

Information Theory Based Evaluation of the RC4 Stream Cipher Outputs

Madarro-Capó

Legón-Pérez

Rojas

et al. 2021

Entropy

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This paper presents a criterion, based on information theory, to measure the amount of average information provided by the sequences of outputs of the RC4 on the internal state. The test statistic used is the sum of the maximum plausible estimates of the entropies H(jt|zt), corresponding to the probability distributions P(jt|zt) of the sequences of random variables (jt)t∈T and (zt)t∈T, independent, but not identically distributed, where zt are the known values of the outputs, while jt is one of the unknown elements of the internal state of the RC4. It is experimentally demonstrated that the test statistic allows for determining the most vulnerable RC4 outputs, and it is proposed to be used as a vulnerability metric for each RC4 output sequence concerning the iterative probabilistic attack.

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Section: Preliminariesmentioning

confidence: 99%

Information Theory Based Evaluation of the RC4 Stream Cipher Outputs

Madarro-Capó

Legón-Pérez

Rojas

et al. 2021

Entropy

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“…Maximizing our margin is important, hence we are intrigued in using support vector machines and concurrent hyperplanes. Using equation (5) to describe parallel hyperplanes…”

Section: ( )} (3)mentioning

confidence: 99%

“…With this method, it doesn't matter whether someone has security expertise since the attacker just has to fool the biometric characteristic of the authorised user. Therefore, security systems are often designed to defend against spoof attacks using approaches such as hashing, digital signature, or encryption that are mostly useless in such assaults [5]. Reliable anti-spoofing solutions for biometric characteristics, including fingerprints, face, and other biometric features, have recently received considerable attention in the research community [6].…”

Section: Introductionmentioning

confidence: 99%

A New Hybrid CNN-LSTM Model with Non-SoftMax Functions for Face Spoof Detection

Kumar

Asish

Ganapathy

2021

Preprint

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Recently, the emerging applications such as banking, mobile payments, face recognition technology are gradually booming and also increases the users count around the world. The extensive deployment of facial recognition systems has drawn close attention to the dependability of facial biometrics in the fight against spoof attacks, in which a picture, video or 3D mask of a real user's face may be used to access facilities or services illegitimately. While a number of anti-spoofing or liveness detection approaches (which identify whether a face is live or spoof when captured) were suggested, the problem is still unresolved because of the difficulty in discovering discriminatory and computer-cost characteristics and techniques for spoof assaults. Existing methods also utilise a full picture or video to determine luminosity. Often though, some facial areas (video frames) are redundant or relate to the confusion of the picture (video). In this paper, we propose a new hybrid deep learning technique called Hybrid Convolutional Neural Network (CNN) based architecture with Long Short-Term Memory (LSTM) units to study the impact in classification. In this technique is applied a non-softmax function for making effective decision on classification. The hybrid approach is implemented followed by a comparative analysis with existing conventional and hybrid techniques used for spoof detection. The proposed model is proved as better than the existing deep learning techniques and other hybrid models in terms of precision, recall, f-measure and accuracy.

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“…These algorithms produce a multi-level taxonomy, or clustering of biometrics data, utilizing the dendrogram tree structure. Dendrograms can be constructed top-down or bottom-up, creating two categories of algorithms [133]: divisive and agglomerative. Divisive clustering algorithms build the dendrogram from the root, where the entire samples enter a single cluster and are successively divided by the worst cluster until every cluster comprises just a single sample.…”

Section: Hierarchical Clusteringmentioning

confidence: 99%

Pattern Mining Approaches Used in Sensor-Based Biometric Recognition: A Review

et al. 2019

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Sensing technologies place significant interest in the use of biometrics for the recognition and assessment of individuals. Pattern mining techniques have established a critical step in the progress of sensor-based biometric systems that are capable of perceiving, recognizing and computing sensor data, being a technology that searches for the high-level information about pattern recognition from low-level sensor readings in order to construct an artificial substitute for human recognition. The design of a successful sensor-based biometric recognition system needs to pay attention to the different issues involved in processing variable data being-acquisition of biometric data from a sensor, data pre-processing, feature extraction, recognition and/or classification, clustering and validation. A significant number of approaches from image processing, pattern identification and machine learning have been used to process sensor data. This paper aims to deliver a state-of-the-art summary and present strategies for utilizing the broadly utilized pattern mining methods in order to identify the challenges as well as future research directions of sensor-based biometric systems.

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Security and Cryptographic Challenges for Authentication Based on Biometrics Data

Cited by 12 publications

References 4 publications

Information Theory Based Evaluation of the RC4 Stream Cipher Outputs

Information Theory Based Evaluation of the RC4 Stream Cipher Outputs

A New Hybrid CNN-LSTM Model with Non-SoftMax Functions for Face Spoof Detection

Pattern Mining Approaches Used in Sensor-Based Biometric Recognition: A Review

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