Novel Physical Layer Authentication Techniques for Secure Wireless Communications

Due to the open nature of radio propagation, information security in wireless communications has been facing more challenges compared to its counterpart in wired networks. Authentication, defined as an important aspect of information security, is the process of verifying the identity of transmitters to prevent against spoofing attacks. Traditionally, secure wireless communications is achieved by relying solely upon higher layer cryptographic mechanisms. However, cryptographic approaches based on complex mathematical calculations are inefficient and vulnerable to various types of attacks. Recently, researchers have shown that the unique properties of wireless channels can be exploited for authentication enhancement by providing additional security protection against spoofing attacks. Motivated by the vulnerability of existing higher-layer security techniques and the security advantages provided by exploring the physical link properties, five novel physical layer authentication techniques to enhance the security performance of wireless systems are proposed. The first technique exploits the inherent properties of CIR to achieve robust channel-based authentication. The second and third techniques utilize a long-range channel predictor and additional multipath delay characteristics, respectively, to enhance the CIR-based authentication. The fourth technique exploits the advantages of AF cooperative relaying to improve traditional channel-based authentication. The last technique employs an embedded confidential signaling link to secure the legitimate transmissions in OFDM systems

Análise de propriedades intrínsecas e extrínsecas de amostras biométricas para detecção de ataques de apresentação

Orientadores: Anderson de Rezende Rocha, Hélio PedriniTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Os recentes avanços nas áreas de pesquisa em biometria, forense e segurança da informação trouxeram importantes melhorias na eficácia dos sistemas de reconhecimento biométricos. No entanto, um desafio ainda em aberto é a vulnerabilidade de tais sistemas contra ataques de apresentação, nos quais os usuários impostores criam amostras sintéticas, a partir das informações biométricas originais de um usuário legítimo, e as apresentam ao sensor de aquisição procurando se autenticar como um usuário válido. Dependendo da modalidade biométrica, os tipos de ataque variam de acordo com o tipo de material usado para construir as amostras sintéticas. Por exemplo, em biometria facial, uma tentativa de ataque é caracterizada quando um usuário impostor apresenta ao sensor de aquisição uma fotografia, um vídeo digital ou uma máscara 3D com as informações faciais de um usuário-alvo. Em sistemas de biometria baseados em íris, os ataques de apresentação podem ser realizados com fotografias impressas ou com lentes de contato contendo os padrões de íris de um usuário-alvo ou mesmo padrões de textura sintéticas. Nos sistemas biométricos de impressão digital, os usuários impostores podem enganar o sensor biométrico usando réplicas dos padrões de impressão digital construídas com materiais sintéticos, como látex, massa de modelar, silicone, entre outros. Esta pesquisa teve como objetivo o desenvolvimento de soluções para detecção de ataques de apresentação considerando os sistemas biométricos faciais, de íris e de impressão digital. As linhas de investigação apresentadas nesta tese incluem o desenvolvimento de representações baseadas nas informações espaciais, temporais e espectrais da assinatura de ruído; em propriedades intrínsecas das amostras biométricas (e.g., mapas de albedo, de reflectância e de profundidade) e em técnicas de aprendizagem supervisionada de características. Os principais resultados e contribuições apresentadas nesta tese incluem: a criação de um grande conjunto de dados publicamente disponível contendo aproximadamente 17K videos de simulações de ataques de apresentações e de acessos genuínos em um sistema biométrico facial, os quais foram coletados com a autorização do Comitê de Ética em Pesquisa da Unicamp; o desenvolvimento de novas abordagens para modelagem e análise de propriedades extrínsecas das amostras biométricas relacionadas aos artefatos que são adicionados durante a fabricação das amostras sintéticas e sua captura pelo sensor de aquisição, cujos resultados de desempenho foram superiores a diversos métodos propostos na literature que se utilizam de métodos tradicionais de análise de images (e.g., análise de textura); a investigação de uma abordagem baseada na análise de propriedades intrínsecas das faces, estimadas a partir da informação de sombras presentes em sua superfície; e, por fim, a investigação de diferentes abordagens baseadas em redes neurais convolucionais para o aprendizado automático de características relacionadas ao nosso problema, cujos resultados foram superiores ou competitivos aos métodos considerados estado da arte para as diferentes modalidades biométricas consideradas nesta tese. A pesquisa também considerou o projeto de eficientes redes neurais com arquiteturas rasas capazes de aprender características relacionadas ao nosso problema a partir de pequenos conjuntos de dados disponíveis para o desenvolvimento e a avaliação de soluções para a detecção de ataques de apresentaçãoAbstract: Recent advances in biometrics, information forensics, and security have improved the recognition effectiveness of biometric systems. However, an ever-growing challenge is the vulnerability of such systems against presentation attacks, in which impostor users create synthetic samples from the original biometric information of a legitimate user and show them to the acquisition sensor seeking to authenticate themselves as legitimate users. Depending on the trait used by the biometric authentication, the attack types vary with the type of material used to build the synthetic samples. For instance, in facial biometric systems, an attempted attack is characterized by the type of material the impostor uses such as a photograph, a digital video, or a 3D mask with the facial information of a target user. In iris-based biometrics, presentation attacks can be accomplished with printout photographs or with contact lenses containing the iris patterns of a target user or even synthetic texture patterns. In fingerprint biometric systems, impostor users can deceive the authentication process using replicas of the fingerprint patterns built with synthetic materials such as latex, play-doh, silicone, among others. This research aimed at developing presentation attack detection (PAD) solutions whose objective is to detect attempted attacks considering different attack types, in each modality. The lines of investigation presented in this thesis aimed at devising and developing representations based on spatial, temporal and spectral information from noise signature, intrinsic properties of the biometric data (e.g., albedo, reflectance, and depth maps), and supervised feature learning techniques, taking into account different testing scenarios including cross-sensor, intra-, and inter-dataset scenarios. The main findings and contributions presented in this thesis include: the creation of a large and publicly available benchmark containing 17K videos of presentation attacks and bona-fide presentations simulations in a facial biometric system, whose collect were formally authorized by the Research Ethics Committee at Unicamp; the development of novel approaches to modeling and analysis of extrinsic properties of biometric samples related to artifacts added during the manufacturing of the synthetic samples and their capture by the acquisition sensor, whose results were superior to several approaches published in the literature that use traditional methods for image analysis (e.g., texture-based analysis); the investigation of an approach based on the analysis of intrinsic properties of faces, estimated from the information of shadows present on their surface; and the investigation of different approaches to automatically learning representations related to our problem, whose results were superior or competitive to state-of-the-art methods for the biometric modalities considered in this thesis. We also considered in this research the design of efficient neural networks with shallow architectures capable of learning characteristics related to our problem from small sets of data available to develop and evaluate PAD solutionsDoutoradoCiência da ComputaçãoDoutor em Ciência da Computação140069/2016-0 CNPq, 142110/2017-5CAPESCNP

Face Liveness Detection under Processed Image Attacks

Face recognition is a mature and reliable technology for identifying people. Due to high-definition cameras and supporting devices, it is considered the fastest and the least intrusive biometric recognition modality. Nevertheless, effective spoofing attempts on face recognition systems were found to be possible. As a result, various anti-spoofing algorithms were developed to counteract these attacks. They are commonly referred in the literature a liveness detection tests. In this research we highlight the effectiveness of some simple, direct spoofing attacks, and test one of the current robust liveness detection algorithms, i.e. the logistic regression based face liveness detection from a single image, proposed by the Tan et al. in 2010, against malicious attacks using processed imposter images. In particular, we study experimentally the effect of common image processing operations such as sharpening and smoothing, as well as corruption with salt and pepper noise, on the face liveness detection algorithm, and we find that it is especially vulnerable against spoofing attempts using processed imposter images. We design and present a new facial database, the Durham Face Database, which is the first, to the best of our knowledge, to have client, imposter as well as processed imposter images. Finally, we evaluate our claim on the effectiveness of proposed imposter image attacks using transfer learning on Convolutional Neural Networks. We verify that such attacks are more difficult to detect even when using high-end, expensive machine learning techniques

Efficient Latent Semantic Extraction from Cross Domain Data with Declarative Language

With large amounts of data continuously generated by intelligence devices, efficient analysis of huge data collections to unearth valuable insights has become one of the most elusive challenges for both academia and industry. The key elements to establishing a scalable analyzing framework should involve (1) an intuitive interface to describe the desired outcome, (2) a well-crafted model that integrates all available information sources to derive the optimal outcome and (3) an efficient algorithm that performs the data integration and extraction within a reasonable amount of time. In this dissertation, we address these challenges by proposing (1) a cross-language interface for a succinct expression of recursive queries, (2) a domain specific neural network model that can incorporate information of multiple modalities, and (3) a sample efficient training method that can be used even for extremely-large output-class classifiers. Our contributions in this thesis are thus threefold: First, for the ubiquitous recursive queries in advanced data analytics, on top of BigDatalog and Apache Spark, we design a succinct and expressive analytics tool encapsulating the functionality and classical algorithms of Datalog, a quintessential logic programming language. We provide the Logical Library (LLib), a Spark MLlib-like high-level API supporting a wide range of recursive algorithms and the Logical DataFrame (LFrame), an extension to Spark DataFrame supporting both relational and logical operations. The LLib and LFrame enable smooth collaborations between logical applications and other Spark libraries and cross-language logical programming in Scala, Java, or Python. Second, we utilize variants of recurrent neural network (RNN) to incorporate some enlightening sequential information overlooked by the conventional works in two different domains including Spoken Language Understanding (SLU) and Internet Embedding (IE). In SLU, we address the problem caused by solely relying on the first best interpretation (hypothesis) of an audio command through a series of new architectures comprising bidirectional LSTM and pooling layers to jointly utilize the other hypotheses' texts or embedding vectors, which are neglected but with valuable information missed by the first best hypothesis. In IE, we propose the DIP, an extension of RNN, to build up the internet coordinate system with the IP address sequences, which are also unnoticed in conventional distance-based internet embedding algorithms but encode structural information of the network. Both DIP and the integration of all hypotheses bring significant performance improvements for the corresponding downstream tasks. Finally, we investigate the training algorithm for multi-class classifiers with a large output-class size, which is common in deep neural networks and typically implemented as a softmax final layer with one output neuron per each class. To avoid expensive computing the intractable normalizing constant of softmax for each training data point, we analyze the well-known negative sampling and improve it to the amplified negative sampling algorithm, which gains much higher performance with lower training cost