Securing Cloud Storage by Transparent Biometric Cryptography

With the capability of storing huge volumes of data over the Internet, cloud storage has become a popular and desirable service for individuals and enterprises. The security issues, nevertheless, have been the intense debate within the cloud community. Significant attacks can be taken place, the most common being guessing the (poor) passwords. Given weaknesses with verification credentials, malicious attacks have happened across a variety of well-known storage services (i.e. Dropbox and Google Drive) – resulting in loss the privacy and confidentiality of files. Whilst today's use of third-party cryptographic applications can independently encrypt data, it arguably places a significant burden upon the user in terms of manually ciphering/deciphering each file and administering numerous keys in addition to the login password. The field of biometric cryptography applies biometric modalities within cryptography to produce robust bio-crypto keys without having to remember them. There are, nonetheless, still specific flaws associated with the security of the established bio-crypto key and its usability. Users currently should present their biometric modalities intrusively each time a file needs to be encrypted/decrypted – thus leading to cumbersomeness and inconvenience while throughout usage. Transparent biometrics seeks to eliminate the explicit interaction for verification and thereby remove the user inconvenience. However, the application of transparent biometric within bio-cryptography can increase the variability of the biometric sample leading to further challenges on reproducing the bio-crypto key. An innovative bio-cryptographic approach is developed to non-intrusively encrypt/decrypt data by a bio-crypto key established from transparent biometrics on the fly without storing it somewhere using a backpropagation neural network. This approach seeks to handle the shortcomings of the password login, and concurrently removes the usability issues of the third-party cryptographic applications – thus enabling a more secure and usable user-oriented level of encryption to reinforce the security controls within cloud-based storage. The challenge represents the ability of the innovative bio-cryptographic approach to generate a reproducible bio-crypto key by selective transparent biometric modalities including fingerprint, face and keystrokes which are inherently noisier than their traditional counterparts. Accordingly, sets of experiments using functional and practical datasets reflecting a transparent and unconstrained sample collection are conducted to determine the reliability of creating a non-intrusive and repeatable bio-crypto key of a 256-bit length. With numerous samples being acquired in a non-intrusive fashion, the system would be spontaneously able to capture 6 samples within minute window of time. There is a possibility then to trade-off the false rejection against the false acceptance to tackle the high error, as long as the correct key can be generated via at least one successful sample. As such, the experiments demonstrate that a correct key can be generated to the genuine user once a minute and the average FAR was 0.9%, 0.06%, and 0.06% for fingerprint, face, and keystrokes respectively. For further reinforcing the effectiveness of the key generation approach, other sets of experiments are also implemented to determine what impact the multibiometric approach would have upon the performance at the feature phase versus the matching phase. Holistically, the multibiometric key generation approach demonstrates the superiority in generating the bio-crypto key of a 256-bit in comparison with the single biometric approach. In particular, the feature-level fusion outperforms the matching-level fusion at producing the valid correct key with limited illegitimacy attempts in compromising it – 0.02% FAR rate overall. Accordingly, the thesis proposes an innovative bio-cryptosystem architecture by which cloud-independent encryption is provided to protect the users' personal data in a more reliable and usable fashion using non-intrusive multimodal biometrics.Higher Committee of Education Development in Iraq (HCED

Support Vector Machine for Behavior-Based Driver Identification System

We present an intelligent driver identification system to handle vehicle theft based on modeling dynamic human behaviors. We propose to recognize illegitimate drivers through their driving behaviors. Since human driving behaviors belong to a dynamic biometrical feature which is complex and difficult to imitate compared with static features such as passwords and fingerprints, we find that this novel idea of utilizing human dynamic features for enhanced security application is more effective. In this paper, we first describe our experimental platform for collecting and modeling human driving behaviors. Then we compare fast Fourier transform (FFT), principal component analysis (PCA), and independent component analysis (ICA) for data preprocessing. Using machine learning method of support vector machine (SVM), we derive the individual driving behavior model and we then demonstrate the procedure for recognizing different drivers by analyzing the corresponding models. The experimental results of learning algorithms and evaluation are described

Utilizing Analytical Hierarchy Process for Pauper House Programme in Malaysia

In Malaysia, the selection and evaluation of candidates for Pauper House Programme (PHP) are done manually. In this paper, a technique based on Analytical Hierarchy Technique (AHP) is designed and developed in order to make an evaluation and selection of PHP application. The aim is to ensure the selection process is more precise, accurate and can avoid any biasness issue. This technique is studied and designed based on the Pauper assessment technique from one of district offices in Malaysia. A hierarchical indexes are designed based on the criteria that been used in the official form of PHP application. A number of 23 samples of data which had been endorsed by Exco of State in Malaysia are used to test this technique. Furthermore the comparison of those two methods are given in this paper. All the calculations of this technique are done in a software namely Expert Choice version 11.5. By comparing the manual and AHP shows that there are three (3) samples that are not qualified. The developed technique also satisfies in term of ease of accuracy and preciseness but need a further study due to some limitation as explained in the recommendation of this paper

Information Encoding for Flow Watermarking and Binding Keys to Biometric Data

Due to the current level of telecommunications development, fifth-generation (5G) communication systems are expected to provide higher data rates, lower latency, and improved scalability. To ensure the security and reliability of data traffic generated from wireless sources, 5G networks must be designed to support security protocols and reliable communication applications. The operations of coding and processing of information during the transmission of both binary and non-binary data in nonstandard communication channels are described. A subclass of linear binary codes is considered, which are both Varshamov-Tenengolz codes and are used for channels with insertions and deletions of symbols. The use of these codes is compared with Hidden Markov Model (HMM)-based systems for detecting intrusions in networks using flow watermarking, which provide high true positive rate in both cases. The principles of using Bose-Chadhuri-Hocquenhgem (BCH) codes, non-binary Reed-Solomon codes, and turbo codes, as well as concatenated code structures to ensure noise immunity when reproducing information in Helper-Data Systems are considered. Examples of biometric systems organization based on the use of these codes, operating on the basis of the Fuzzy Commitment Scheme (FCS) and providing FRR < 1% for authentication, are given

Development of traceability solution for furniture components

Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáIn the contemporary context, characterized by intensified global competition and the constant evolution of the globalization landscape, it becomes imperative for industries, including Small and Medium Enterprises (SMEs), to undertake efforts to enhance their operational processes, often through digital technological adaptation. The present study falls within the scope of the project named “Wood Work 4.0,” which aims to infuse innovation into the wood furniture manufacturing industry through process optimization and the adoption of digital technologies. This project received funding from the European Union Development Fund, in collaboration with the North 2020 Regional Program, and was carried out at the Carpintaria Mofreita company, located in Macedo de Cavaleiros, Portugal. In this regard, this study introduces a software architecture that supports the traceability of projects in the wood furniture industry and simultaneously employs a system to identify and manage material leftovers, aiming for more efficient waste management. For the development of this software architecture, an approach that integrates the Fiware platform, specialized in systems for the Internet of Things (IoT), with an Application Programming Interface (API) specifically created to manage information about users, projects, and associated media files, was adopted. The material leftovers identification system employs image processing techniques to extract geometric characteristics of the materials. Additionally, these data are integrated into the company’s database. In this way, it was possible to develop an architecture that allows not only the capturing of project information but also its effective management. In the case of material leftovers identification, the system was able to establish, with a satisfactory degree of accuracy, the dimensions of the materials, enabling the insertion of these data into the company’s database for resource management and optimization.No contexto contemporâneo, marcado por uma competição global intensificada e pela constante evolução do cenário de globalização, torna-se imperativo para as indústrias, incluindo as Pequenas e Médias Empresas (PMEs), empreender esforços para aprimorar seus processos operacionais, frequentemente pela via da adaptação tecnológica digital. O presente estudo insere-se dentro do escopo do projeto denominado “Wood Work 4.0”, cujo propósito é infundir inovação na indústria de fabricação de móveis de madeira por meio da otimização de processos e da adoção de tecnologias digitais. Este projeto obteve financiamento do Fundo de Desenvolvimento da União Europeia, em colaboração com o programa Regional do Norte 2020 e foi realizado na empresa Carpintaria Mofreita, localizada em Macedo de Cavaleiros, Portugal. Nesse sentido, este estudo introduz uma arquitetura de software que oferece suporte à rastreabilidade de projetos na indústria de móveis de madeira, e simultaneamente emprega um sistema para identificar e gerenciar sobras de material, objetivando uma gestão de resíduos mais eficiente. Para o desenvolvimento dessa arquitetura de software, adotou-se uma abordagem que integra a plataforma Fiware, especializada em sistemas para a Internet das Coisas (IoT), com uma Interface de Programação de Aplicações (API) criada especificamente para gerenciar informações de usuários, projetos, e arquivos de mídia associados. O sistema de identificação de sobras de material emprega técnicas de processamento de imagem para extrair características geométricas dos materiais. Adicionalmente, esses dados são integrados ao banco de dados da empresa. Desta forma, foi possível desenvolver uma arquitetura que permite não só capturar informações de projetos, mas também gerenciá-las de forma eficaz. No caso da identificação de sobras de material, o sistema foi capaz de estabelecer, com um grau de precisão satisfatório, as dimensões dos materiais, possibilitando a inserção desses dados no banco de dados da empresa para gestão e otimização do uso de recursos

Voice signature based Speaker Recognition

Magister Scientiae - MSc (Computer Science)Personal identification and the protection of data are important issues because of the ubiquitousness of computing and these havethus become interesting areas of research in the field of computer science. Previously people have used a variety of ways to identify an individual and protect themselves, their property and their information