Special Issue "Biometric and Bio-inspired Approaches in Cryptography"

A special issue of Cryptography (ISSN 2410-387X).

Deadline for manuscript submissions: 30 June 2018

Special Issue Editor

Guest Editor
Prof. Dr. Marek R. Ogiela

Cryptography and Cognitive Informatics Research Group, AGH University of Science and Technology, 30 Mickiewicza Ave, 30-059 Krakow, Poland
Website | E-Mail
Interests: secret sharing; steganography; visual cryptography; subliminal channel; cognitive cryptography; biometric in cryptography

Special Issue Information

Dear Colleagues,

The great progress in security and computing has been made possible thanks to the applications of advanced soft computing approaches, and other algorithmic techniques, such as artificial intelligence, bio-inspired computation, etc. Recently, such techniques have had a great importance, especially in areas of intelligent and secure analysis of a great amount of data orginating from the Cloud, the Internet of Things, and different sources connected with pervasive and mobile computing. The possibility of development of such new technologies will depend on many different factors like new computational paradigms, which implement innovative security models for secure communication and pervasive computation. It may also depend on proper analysis of a wider context around the systems, and methods providing a high level of security and confidentiality of transmitted data. However, one of the most important aspect, which will also have an infuence on modern security protocols, is appplication biometric patterns, and bio-inspired computational models, which allow to create new branches in personalized cryptography.

Such subjects, as well as a number of others, like innovative and secure applications, as well as ways of using innovative biometric and personalized protocols and computational models to develop new solutions in the field of secure communication protocols, wireless transmissions, or pervasive computing, will form the subject of a Special Issue on “Biometric and Bio-inspired Approches in Cryptography” in Cryptography.

Prof. Dr. Marek R. Ogiela
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cryptography is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) is waived for well-prepared manuscripts submitted to this issue. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Bio-inspired approaches in cryptography
  • Cognitive information systems for secure information distribution and management
  • Cognitive keys in cryptography
  • Computational intelligence in security services
  • Biometrics in secure pervasive computing
  • Personalized cryptography in ubiquitous computing
  • Visual and cartoon captcha
  • Multi-secret steganography
  • Personal features for behavioral lock
  • Standard and non-standard biometrics in cryptographic application

Published Papers (5 papers)

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Research

Open AccessArticle Multi-Factor Authentication: A Survey
Received: 30 November 2017 / Revised: 17 December 2017 / Accepted: 18 December 2017 / Published: 5 January 2018
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Abstract
Today, digitalization decisively penetrates all the sides of the modern society. One of the key enablers to maintain this process secure is authentication. It covers many different areas of a hyper-connected world, including online payments, communications, access right management, etc. This work sheds
[...] Read more.
Today, digitalization decisively penetrates all the sides of the modern society. One of the key enablers to maintain this process secure is authentication. It covers many different areas of a hyper-connected world, including online payments, communications, access right management, etc. This work sheds light on the evolution of authentication systems towards Multi-Factor Authentication (MFA) starting from Single-Factor Authentication (SFA) and through Two-Factor Authentication (2FA). Particularly, MFA is expected to be utilized for human-to-everything interactions by enabling fast, user-friendly, and reliable authentication when accessing a service. This paper surveys the already available and emerging sensors (factor providers) that allow for authenticating a user with the system directly or by involving the cloud. The corresponding challenges from the user as well as the service provider perspective are also reviewed. The MFA system based on reversed Lagrange polynomial within Shamir’s Secret Sharing (SSS) scheme is further proposed to enable more flexible authentication. This solution covers the cases of authenticating the user even if some of the factors are mismatched or absent. Our framework allows for qualifying the missing factors by authenticating the user without disclosing sensitive biometric data to the verification entity. Finally, a vision of the future trends in MFA is discussed. Full article
(This article belongs to the Special Issue Biometric and Bio-inspired Approaches in Cryptography)
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Graphical abstract

Open AccessArticle Anomalous Traffic Detection and Self-Similarity Analysis in the Environment of ATMSim
Cryptography 2017, 1(3), 24; https://doi.org/10.3390/cryptography1030024
Received: 29 October 2017 / Revised: 3 December 2017 / Accepted: 6 December 2017 / Published: 12 December 2017
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Abstract
Internet utilisation has steadily increased, predominantly due to the rapid recent development of information and communication networks and the widespread distribution of smartphones. As a result of this increase in Internet consumption, various types of services, including web services, social networking services (SNS),
[...] Read more.
Internet utilisation has steadily increased, predominantly due to the rapid recent development of information and communication networks and the widespread distribution of smartphones. As a result of this increase in Internet consumption, various types of services, including web services, social networking services (SNS), Internet banking, and remote processing systems have been created. These services have significantly enhanced global quality of life. However, as a negative side-effect of this rapid development, serious information security problems have also surfaced, which has led to serious to Internet privacy invasions and network attacks. In an attempt to contribute to the process of addressing these problems, this paper proposes a process to detect anomalous traffic using self-similarity analysis in the Anomaly Teletraffic detection Measurement analysis Simulator (ATMSim) environment as a research method. Simulations were performed to measure normal and anomalous traffic. First, normal traffic for each attack, including the Address Resolution Protocol (ARP) and distributed denial-of-service (DDoS) was measured for 48 h over 10 iterations. Hadoop was used to facilitate processing of the large amount of collected data, after which MapReduce was utilised after storing the data in the Hadoop Distributed File System (HDFS). A new platform on Hadoop, the detection system ATMSim, was used to identify anomalous traffic after which a comparative analysis of the normal and anomalous traffic was performed through a self-similarity analysis. There were four categories of collected traffic that were divided according to the attack methods used: normal local area network (LAN) traffic, DDoS attack, and ARP spoofing, as well as DDoS and ARP attack. ATMSim, the anomaly traffic detection system, was used to determine if real attacks could be identified effectively. To achieve this, the ATMSim was used in simulations for each scenario to test its ability to distinguish between normal and anomalous traffic. The graphic and quantitative analyses in this study, based on the self-similarity estimation for the four different traffic types, showed a burstiness phenomenon when anomalous traffic occurred and self-similarity values were high. This differed significantly from the results obtained when normal traffic, such as LAN traffic, occurred. In further studies, this anomaly detection approach can be utilised with biologically inspired techniques that can predict behaviour, such as the artificial neural network (ANN) or fuzzy approach. Full article
(This article belongs to the Special Issue Biometric and Bio-inspired Approaches in Cryptography)
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Open AccessArticle Learning Global-Local Distance Metrics for Signature-Based Biometric Cryptosystems
Cryptography 2017, 1(3), 22; https://doi.org/10.3390/cryptography1030022
Received: 24 October 2017 / Revised: 20 November 2017 / Accepted: 21 November 2017 / Published: 25 November 2017
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Abstract
Biometric traits, such as fingerprints, faces and signatures have been employed in bio-cryptosystems to secure cryptographic keys within digital security schemes. Reliable implementations of these systems employ error correction codes formulated as simple distance thresholds, although they may not effectively model the complex
[...] Read more.
Biometric traits, such as fingerprints, faces and signatures have been employed in bio-cryptosystems to secure cryptographic keys within digital security schemes. Reliable implementations of these systems employ error correction codes formulated as simple distance thresholds, although they may not effectively model the complex variability of behavioral biometrics like signatures. In this paper, a Global-Local Distance Metric (GLDM) framework is proposed to learn cost-effective distance metrics, which reduce within-class variability and augment between-class variability, so that simple error correction thresholds of bio-cryptosystems provide high classification accuracy. First, a large number of samples from a development dataset are used to train a global distance metric that differentiates within-class from between-class samples of the population. Then, once user-specific samples are available for enrollment, the global metric is tuned to a local user-specific one. Proof-of-concept experiments on two reference offline signature databases confirm the viability of the proposed approach. Distance metrics are produced based on concise signature representations consisting of about 20 features and a single prototype. A signature-based bio-cryptosystem is designed using the produced metrics and has shown average classification error rates of about 7% and 17% for the PUCPR and the GPDS-300 databases, respectively. This level of performance is comparable to that obtained with complex state-of-the-art classifiers. Full article
(This article belongs to the Special Issue Biometric and Bio-inspired Approaches in Cryptography)
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Open AccessArticle A Cryptographic System Based upon the Principles of Gene Expression
Cryptography 2017, 1(3), 21; https://doi.org/10.3390/cryptography1030021
Received: 21 October 2017 / Revised: 13 November 2017 / Accepted: 16 November 2017 / Published: 21 November 2017
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Abstract
Processes of gene expression such as regulation of transcription by the general transcription complex can be used to create hard cryptographic protocols which should not be breakable by common cipherattack methodologies. The eukaryotic processes of gene expression permit expansion of DNA cryptography into
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Processes of gene expression such as regulation of transcription by the general transcription complex can be used to create hard cryptographic protocols which should not be breakable by common cipherattack methodologies. The eukaryotic processes of gene expression permit expansion of DNA cryptography into complex networks of transcriptional and translational coding interactions. I describe a method of coding messages into genes and their regulatory sequences, transcription products, regulatory protein complexes, transcription proteins, translation proteins and other required sequences. These codes then serve as the basis for a cryptographic model based on the processes of gene expression. The protocol provides a hierarchal structure that extends from the initial coding of a message into a DNA code (ciphergene), through transcription and ultimately translation into a protein code (cipherprotein). The security is based upon unique knowledge of the DNA coding process, all of the regulatory codes required for expression, and their interactions. This results in a set of cryptographic protocols that is capable of securing data at rest, data in motion and providing an evolvable form of security between two or more parties. The conclusion is that implementation of these protocols will enhance security and substantially burden cyberattackers to develop new forms of countermeasures. Full article
(This article belongs to the Special Issue Biometric and Bio-inspired Approaches in Cryptography)
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Open AccessArticle A Text-Independent Speaker Authentication System for Mobile Devices
Cryptography 2017, 1(3), 16; https://doi.org/10.3390/cryptography1030016
Received: 6 July 2017 / Revised: 12 September 2017 / Accepted: 19 September 2017 / Published: 22 September 2017
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Abstract
This paper presents a text independent speaker authentication method adapted to mobile devices. Special attention was placed on delivering a fully operational application, which admits a sufficient reliability level and an efficient functioning. To this end, we have excluded the need for any
[...] Read more.
This paper presents a text independent speaker authentication method adapted to mobile devices. Special attention was placed on delivering a fully operational application, which admits a sufficient reliability level and an efficient functioning. To this end, we have excluded the need for any network communication. Hence, we opted for the completion of both the training and the identification processes directly on the mobile device through the extraction of linear prediction cepstral coefficients and the naive Bayes algorithm as the classifier. Furthermore, the authentication decision is enhanced to overcome misidentification through access privileges that the user should attribute to each application beforehand. To evaluate the proposed authentication system, eleven participants were involved in the experiment, conducted in quiet and noisy environments. Public speech corpora were also employed to compare this implementation to existing methods. Results were efficient regarding mobile resources’ consumption. The overall classification performance obtained was accurate with a small number of samples. Then, it appeared that our authentication system might be used as a first security layer, but also as part of a multilayer authentication, or as a fall-back mechanism. Full article
(This article belongs to the Special Issue Biometric and Bio-inspired Approaches in Cryptography)
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