38 research outputs found

    Poster: Continual Network Learning

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    We make a case for in-network Continual Learning as a solution for seamless adaptation to evolving network conditions without forgetting past experiences. We propose implementing Active Learning-based selective data filtering in the data plane, allowing for data-efficient continual updates. We explore relevant challenges and propose future research directions

    Face recognition by means of advanced contributions in machine learning

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    Face recognition (FR) has been extensively studied, due to both scientific fundamental challenges and current and potential applications where human identification is needed. FR systems have the benefits of their non intrusiveness, low cost of equipments and no useragreement requirements when doing acquisition, among the most important ones. Nevertheless, despite the progress made in last years and the different solutions proposed, FR performance is not yet satisfactory when more demanding conditions are required (different viewpoints, blocked effects, illumination changes, strong lighting states, etc). Particularly, the effect of such non-controlled lighting conditions on face images leads to one of the strongest distortions in facial appearance. This dissertation addresses the problem of FR when dealing with less constrained illumination situations. In order to approach the problem, a new multi-session and multi-spectral face database has been acquired in visible, Near-infrared (NIR) and Thermal infrared (TIR) spectra, under different lighting conditions. A theoretical analysis using information theory to demonstrate the complementarities between different spectral bands have been firstly carried out. The optimal exploitation of the information provided by the set of multispectral images has been subsequently addressed by using multimodal matching score fusion techniques that efficiently synthesize complementary meaningful information among different spectra. Due to peculiarities in thermal images, a specific face segmentation algorithm has been required and developed. In the final proposed system, the Discrete Cosine Transform as dimensionality reduction tool and a fractional distance for matching were used, so that the cost in processing time and memory was significantly reduced. Prior to this classification task, a selection of the relevant frequency bands is proposed in order to optimize the overall system, based on identifying and maximizing independence relations by means of discriminability criteria. The system has been extensively evaluated on the multispectral face database specifically performed for our purpose. On this regard, a new visualization procedure has been suggested in order to combine different bands for establishing valid comparisons and giving statistical information about the significance of the results. This experimental framework has more easily enabled the improvement of robustness against training and testing illumination mismatch. Additionally, focusing problem in thermal spectrum has been also addressed, firstly, for the more general case of the thermal images (or thermograms), and then for the case of facialthermograms from both theoretical and practical point of view. In order to analyze the quality of such facial thermograms degraded by blurring, an appropriate algorithm has been successfully developed. Experimental results strongly support the proposed multispectral facial image fusion, achieving very high performance in several conditions. These results represent a new advance in providing a robust matching across changes in illumination, further inspiring highly accurate FR approaches in practical scenarios.El reconeixement facial (FR) ha estat àmpliament estudiat, degut tant als reptes fonamentals científics que suposa com a les aplicacions actuals i futures on requereix la identificació de les persones. Els sistemes de reconeixement facial tenen els avantatges de ser no intrusius,presentar un baix cost dels equips d’adquisició i no la no necessitat d’autorització per part de l’individu a l’hora de realitzar l'adquisició, entre les més importants. De totes maneres i malgrat els avenços aconseguits en els darrers anys i les diferents solucions proposades, el rendiment del FR encara no resulta satisfactori quan es requereixen condicions més exigents (diferents punts de vista, efectes de bloqueig, canvis en la il·luminació, condicions de llum extremes, etc.). Concretament, l'efecte d'aquestes variacions no controlades en les condicions d'il·luminació sobre les imatges facials condueix a una de les distorsions més accentuades sobre l'aparença facial. Aquesta tesi aborda el problema del FR en condicions d'il·luminació menys restringides. Per tal d'abordar el problema, hem adquirit una nova base de dades de cara multisessió i multiespectral en l'espectre infraroig visible, infraroig proper (NIR) i tèrmic (TIR), sota diferents condicions d'il·luminació. En primer lloc s'ha dut a terme una anàlisi teòrica utilitzant la teoria de la informació per demostrar la complementarietat entre les diferents bandes espectrals objecte d’estudi. L'òptim aprofitament de la informació proporcionada pel conjunt d'imatges multiespectrals s'ha abordat posteriorment mitjançant l'ús de tècniques de fusió de puntuació multimodals, capaces de sintetitzar de manera eficient el conjunt d’informació significativa complementària entre els diferents espectres. A causa de les característiques particulars de les imatges tèrmiques, s’ha requerit del desenvolupament d’un algorisme específic per la segmentació de les mateixes. En el sistema proposat final, s’ha utilitzat com a eina de reducció de la dimensionalitat de les imatges, la Transformada del Cosinus Discreta i una distància fraccional per realitzar les tasques de classificació de manera que el cost en temps de processament i de memòria es va reduir de forma significa. Prèviament a aquesta tasca de classificació, es proposa una selecció de les bandes de freqüències més rellevants, basat en la identificació i la maximització de les relacions d'independència per mitjà de criteris discriminabilitat, per tal d'optimitzar el conjunt del sistema. El sistema ha estat àmpliament avaluat sobre la base de dades de cara multiespectral, desenvolupada pel nostre propòsit. En aquest sentit s'ha suggerit l’ús d’un nou procediment de visualització per combinar diferents bandes per poder establir comparacions vàlides i donar informació estadística sobre el significat dels resultats. Aquest marc experimental ha permès més fàcilment la millora de la robustesa quan les condicions d’il·luminació eren diferents entre els processos d’entrament i test. De forma complementària, s’ha tractat la problemàtica de l’enfocament de les imatges en l'espectre tèrmic, en primer lloc, pel cas general de les imatges tèrmiques (o termogrames) i posteriorment pel cas concret dels termogrames facials, des dels punt de vista tant teòric com pràctic. En aquest sentit i per tal d'analitzar la qualitat d’aquests termogrames facials degradats per efectes de desenfocament, s'ha desenvolupat un últim algorisme. Els resultats experimentals recolzen fermament que la fusió d'imatges facials multiespectrals proposada assoleix un rendiment molt alt en diverses condicions d’il·luminació. Aquests resultats representen un nou avenç en l’aportació de solucions robustes quan es contemplen canvis en la il·luminació, i esperen poder inspirar a futures implementacions de sistemes de reconeixement facial precisos en escenaris no controlats.Postprint (published version

    Automated border control systems: biometric challenges and research trends

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    Automated Border Control (ABC) systems automatically verify the travelers\u2019 identity using their biometric information, without the need of a manual check, by comparing the data stored in the electronic document (e.g., the e-Passport) with a live sample captured during the crossing of the border. In this paper, the hardware and software components of the biometric systems used in ABC systems are described, along with the latest challenges and research trends

    DYNAMIC BIOMETRIC SIGNATURE AS AN EFFICIENT TOOL FOR INTERNAL CORPORATE COMMUNICATION

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    Cílem práce je podat ucelenou informaci o možnostech autentizace, kombinace autentizačních faktorů a začlenění této problematiky do podnikové komunikace. Práce se zaměřuje na tuto problematiku a specifikuje možnosti získání autentizačních informací, dále analyzuje metody autentizace, identifikace a autorizace. Zkoumaná je využitelnost biometrických technologií, princip funkčnosti, příklady jejich použití, jejich vliv, výhody a nevýhody, které přinášejí. Přirozený, snadno dostupný, a tedy vhodný nástroj pro efektivní a bezpečnou komunikaci je autentizace zahrnující dynamický biometrický podpis. Problematika technologie dynamického biometrického podpisu a jeho implementace jsou zkoumány z komplexního hlediska včetně provedených experimentů. Z výzkumu vyplývá, že dynamický biometrický podpis dokáže sloužit jako metoda podporující bezpečnou podnikovou komunikaci a zredukovat autentizační rizika ve společnostech i pro jednotlivce.The aim of this thesis is to provide comprehensive information on the possibilities of authentication, combination of authentication factors and the integration of this issue into corporate communication. The work focuses on this issue and specifies the possibilities for obtaining authentication information, analyses the authentication methods, identification and authorization. It examines the applicability of biometric technologies, the principle of their functionality, examples of their use, their impact, the advantages and disadvantages they bring. A natural, easy-to-use, convenient tool for effective and secure communication is authentication including the dynamic biometric signature. The issues of the dynamic biometric signature technology and its implementation are examined from a comprehensive perspective involving experiments. The research proved that the dynamic biometric signature can serve as a method for supporting secure corporate communication and reduce authentication risks in companies and for individuals.

    DroidDetectMW: A Hybrid Intelligent Model for Android Malware Detection

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    Malicious apps specifically aimed at the Android platform have increased in tandem with the proliferation of mobile devices. Malware is now so carefully written that it is difficult to detect. Due to the exponential growth in malware, manual methods of malware are increasingly ineffective. Although prior writers have proposed numerous high-quality approaches, static and dynamic assessments inherently necessitate intricate procedures. The obfuscation methods used by modern malware are incredibly complex and clever. As a result, it cannot be detected using only static malware analysis. As a result, this work presents a hybrid analysis approach, partially tailored for multiple-feature data, for identifying Android malware and classifying malware families to improve Android malware detection and classification. This paper offers a hybrid method that combines static and dynamic malware analysis to give a full view of the threat. Three distinct phases make up the framework proposed in this research. Normalization and feature extraction procedures are used in the first phase of pre-processing. Both static and dynamic features undergo feature selection in the second phase. Two feature selection strategies are proposed to choose the best subset of features to use for both static and dynamic features. The third phase involves applying a newly proposed detection model to classify android apps; this model uses a neural network optimized with an improved version of HHO. Application of binary and multi-class classification is used, with binary classification for benign and malware apps and multi-class classification for detecting malware categories and families. By utilizing the features gleaned from static and dynamic malware analysis, several machine-learning methods are used for malware classification. According to the results of the experiments, the hybrid approach improves the accuracy of detection and classification of Android malware compared to the scenario when considering static and dynamic information separately

    Wrist vascular biometric recognition using a portable contactless system

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    Human wrist vein biometric recognition is one of the least used vascular biometric modalities. Nevertheless, it has similar usability and is as safe as the two most common vascular variants in the commercial and research worlds: hand palm vein and finger vein modalities. Besides, the wrist vein variant, with wider veins, provides a clearer and better visualization and definition of the unique vein patterns. In this paper, a novel vein wrist non-contact system has been designed, implemented, and tested. For this purpose, a new contactless database has been collected with the software algorithm TGS-CVBR®. The database, called UC3M-CV1, consists of 1200 near-infrared contactless images of 100 different users, collected in two separate sessions, from the wrists of 50 subjects (25 females and 25 males). Environmental light conditions for the different subjects and sessions have been not controlled: different daytimes and different places (outdoor/indoor). The software algorithm created for the recognition task is PIS-CVBR®. The results obtained by combining these three elements, TGS-CVBR®, PIS-CVBR®, and UC3M-CV1 dataset, are compared using two other different wrist contact databases, PUT and UC3M (best value of Equal Error Rate (EER) = 0.08%), taken into account and measured the computing time, demonstrating the viability of obtaining a contactless real-time-processing wrist system.Publicad

    A text-independent speaker authentication system for mobile devices

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    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

    An adaptive and distributed intrusion detection scheme for cloud computing

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    Cloud computing has enormous potentials but still suffers from numerous security issues. Hence, there is a need to safeguard the cloud resources to ensure the security of clients’ data in the cloud. Existing cloud Intrusion Detection System (IDS) suffers from poor detection accuracy due to the dynamic nature of cloud as well as frequent Virtual Machine (VM) migration causing network traffic pattern to undergo changes. This necessitates an adaptive IDS capable of coping with the dynamic network traffic pattern. Therefore, the research developed an adaptive cloud intrusion detection scheme that uses Binary Segmentation change point detection algorithm to track the changes in the normal profile of cloud network traffic and updates the IDS Reference Model when change is detected. Besides, the research addressed the issue of poor detection accuracy due to insignificant features and coordinated attacks such as Distributed Denial of Service (DDoS). The insignificant feature was addressed using feature selection while coordinated attack was addressed using distributed IDS. Ant Colony Optimization and correlation based feature selection were used for feature selection. Meanwhile, distributed Stochastic Gradient Decent and Support Vector Machine (SGD-SVM) were used for the distributed IDS. The distributed IDS comprised detection units and aggregation unit. The detection units detected the attacks using distributed SGD-SVM to create Local Reference Model (LRM) on various computer nodes. Then, the LRM was sent to aggregation units to create a Global Reference Model. This Adaptive and Distributed scheme was evaluated using two datasets: a simulated datasets collected using Virtual Machine Ware (VMWare) hypervisor and Network Security Laboratory-Knowledge Discovery Database (NSLKDD) benchmark intrusion detection datasets. To ensure that the scheme can cope with the dynamic nature of VM migration in cloud, performance evaluation was performed before and during the VM migration scenario. The evaluation results of the adaptive and distributed scheme on simulated datasets showed that before VM migration, an overall classification accuracy of 99.4% was achieved by the scheme while a related scheme achieved an accuracy of 83.4%. During VM migration scenario, classification accuracy of 99.1% was achieved by the scheme while the related scheme achieved an accuracy of 85%. The scheme achieved an accuracy of 99.6% when it was applied to NSL-KDD dataset while the related scheme achieved an accuracy of 83%. The performance comparisons with a related scheme showed that the developed adaptive and distributed scheme achieved superior performance
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