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Design and Implementation of Algorithms for Traffic Classification
Traffic analysis is the practice of using inherent characteristics of a network flow such as timings, sizes, and orderings of the packets to derive sensitive information about it. Traffic analysis techniques are used because of the extensive adoption of encryption and content-obfuscation mechanisms, making it impossible to infer any information about the flows by analyzing their content. In this thesis, we use traffic analysis to infer sensitive information for different objectives and different applications. Specifically, we investigate various applications: p2p cryptocurrencies, flow correlation, and messaging applications. Our goal is to tailor specific traffic analysis algorithms that best capture network traffic’s intrinsic characteristics in those applications for each of these applications. Also, the objective of traffic analysis is different for each of these applications. Specifically, in Bitcoin, our goal is to evaluate Bitcoin traffic’s resilience to blocking by powerful entities such as governments and ISPs. Bitcoin and similar cryptocurrencies play an important role in electronic commerce and other trust-based distributed systems because of their significant advantage over traditional currencies, including open access to global e-commerce. Therefore, it is essential to
the consumers and the industry to have reliable access to their Bitcoin assets. We also examine stepping stone attacks for flow correlation. A stepping stone is a host that an attacker uses to relay her traffic to hide her identity. We introduce two fingerprinting systems, TagIt and FINN. TagIt embeds a secret fingerprint into the flows by moving the packets to specific time intervals. However, FINN utilizes DNNs to embed the fingerprint by changing the inter-packet delays (IPDs) in the flow. In messaging applications, we analyze the WhatsApp messaging service to determine if traffic leaks any sensitive information such as members’ identity in a particular conversation to the adversaries who watch their encrypted traffic. These messaging applications’ privacy is essential because these services provide an environment to dis- cuss politically sensitive subjects, making them a target to government surveillance and censorship in totalitarian countries. We take two technical approaches to design our traffic analysis techniques. The increasing use of DNN-based classifiers inspires our first direction: we train DNN classifiers to perform some specific traffic analysis task. Our second approach is to inspect and model the shape of traffic in the target application and design a statistical classifier for the expected shape of traffic. DNN- based methods are useful when the network is complex, and the traffic’s underlying noise is not linear. Also, these models do not need a meticulous analysis to extract the features. However, deep learning techniques need a vast amount of training data to work well. Therefore, they are not beneficial when there is insufficient data avail- able to train a generalized model. On the other hand, statistical methods have the advantage that they do not have training overhead
BIOMETRIC TECHNOLOGIES FOR AMBIENT INTELLIGENCE
Il termine Ambient Intelligence (AmI) si riferisce a un ambiente in grado di riconoscere e rispondere alla presenza di diversi individui in modo trasparente, non intrusivo e spesso invisibile. In questo tipo di ambiente, le persone sono circondate da interfacce uomo macchina intuitive e integrate in oggetti di ogni tipo. Gli scopi dell\u2019AmI sono quelli di fornire un supporto ai servizi efficiente e di facile utilizzo per accrescere le potenzialit\ue0 degli individui e migliorare l\u2019interazioni uomo-macchina. Le tecnologie di AmI possono essere impiegate in contesti come uffici (smart offices), case (smart homes), ospedali (smart hospitals) e citt\ue0 (smart cities).
Negli scenari di AmI, i sistemi biometrici rappresentano tecnologie abilitanti al fine di progettare servizi personalizzati per individui e gruppi di persone. La biometria \ue8 la scienza che si occupa di stabilire l\u2019identit\ue0 di una persona o di una classe di persone in base agli attributi fisici o comportamentali dell\u2019individuo. Le applicazioni tipiche dei sistemi biometrici includono: controlli di sicurezza, controllo delle frontiere, controllo fisico dell\u2019accesso e autenticazione per dispositivi elettronici. Negli scenari basati su AmI, le tecnologie biometriche devono funzionare in condizioni non controllate e meno vincolate rispetto ai sistemi biometrici comunemente impiegati. Inoltre, in numerosi scenari applicativi, potrebbe essere necessario utilizzare tecniche in grado di funzionare in modo nascosto e non cooperativo. In questo tipo di applicazioni, i campioni biometrici spesso presentano una bassa qualit\ue0 e i metodi di riconoscimento biometrici allo stato dell\u2019arte potrebbero ottenere prestazioni non soddisfacenti.
\uc8 possibile distinguere due modi per migliorare l\u2019applicabilit\ue0 e la diffusione delle tecnologie biometriche negli scenari basati su AmI. Il primo modo consiste nel progettare tecnologie biometriche innovative che siano in grado di funzionare in modo robusto con campioni acquisiti in condizioni non ideali e in presenza di rumore. Il secondo modo consiste nel progettare approcci biometrici multimodali innovativi, in grado di sfruttare a proprio vantaggi tutti i sensori posizionati in un ambiente generico, al fine di ottenere un\u2019elevata accuratezza del riconoscimento ed effettuare autenticazioni continue o periodiche in modo non intrusivo.
Il primo obiettivo di questa tesi \ue8 la progettazione di sistemi biometrici innovativi e scarsamente vincolati in grado di migliorare, rispetto allo stato dell\u2019arte attuale, la qualit\ue0 delle tecniche di interazione uomo-macchine in diversi scenari applicativi basati su AmI. Il secondo obiettivo riguarda la progettazione di approcci innovativi per migliorare l\u2019applicabilit\ue0 e l\u2019integrazione di tecnologie biometriche eterogenee negli scenari che utilizzano AmI. In particolare, questa tesi considera le tecnologie biometriche basate su impronte digitali, volto, voce e sistemi multimodali.
Questa tesi presenta le seguenti ricerche innovative:
\u2022 un metodo per il riconoscimento del parlatore tramite la voce in applicazioni che usano AmI;
\u2022 un metodo per la stima dell\u2019et\ue0 dell\u2019individuo da campioni acquisiti in condizioni non-ideali nell\u2019ambito di scenari basati su AmI;
\u2022 un metodo per accrescere l\u2019accuratezza del riconoscimento biometrico in modo protettivo della privacy e basato sulla normalizzazione degli score biometrici tramite l\u2019analisi di gruppi di campioni simili tra loro;
\u2022 un approccio per la fusione biometrica multimodale indipendente dalla tecnologia utilizzata, in grado di combinare tratti biometrici eterogenei in scenari basati su AmI;
\u2022 un approccio per l\u2019autenticazione continua multimodale in applicazioni che usano AmI.
Le tecnologie biometriche innovative progettate e descritte in questa tesi sono state validate utilizzando diversi dataset biometrici (sia pubblici che acquisiti in laboratorio), i quali simulano le condizioni che si possono verificare in applicazioni di AmI. I risultati ottenuti hanno dimostrato la realizzabilit\ue0 degli approcci studiati e hanno mostrato che i metodi progettati aumentano l\u2019accuratezza, l\u2019applicabilit\ue0 e l\u2019usabilit\ue0 delle tecnologie biometriche rispetto allo stato dell\u2019arte negli scenari basati su AmI.Ambient Intelligence (AmI) refers to an environment capable of recognizing and responding to the presence of different individuals in a seamless, unobtrusive and often invisible way. In this environment, people are surrounded by intelligent intuitive interfaces that are embedded in all kinds of objects. The goals of AmI are to provide greater user-friendliness, more efficient services support, user-empowerment, and support for human interactions. Examples of AmI scenarios are smart cities, smart homes, smart offices, and smart hospitals.
In AmI, biometric technologies represent enabling technologies to design personalized services for individuals or groups of people. Biometrics is the science of establishing the identity of an individual or a class of people based on the physical, or behavioral attributes of the person. Common applications include: security checks, border controls, access control to physical places, and authentication to electronic devices. In AmI, biometric technologies should work in uncontrolled and less-constrained conditions with respect to traditional biometric technologies. Furthermore, in many application scenarios, it could be required to adopt covert and non-cooperative technologies. In these non-ideal conditions, the biometric samples frequently present poor quality, and state-of-the-art biometric technologies can obtain unsatisfactory performance.
There are two possible ways to improve the applicability and diffusion of biometric technologies in AmI. The first one consists in designing novel biometric technologies robust to samples acquire in noisy and non-ideal conditions. The second one consists in designing novel multimodal biometric approaches that are able to take advantage from all the sensors placed in a generic environment in order to achieve high recognition accuracy and to permit to perform continuous or periodic authentications in an unobtrusive manner.
The first goal of this thesis is to design innovative less-constrained biometric systems, which are able to improve the quality of the human-machine interaction in different AmI environments with respect to the state-of-the-art technologies. The second goal is to design novel approaches to improve the applicability and integration of heterogeneous biometric systems in AmI. In particular, the thesis considers technologies based on fingerprint, face, voice, and multimodal biometrics.
This thesis presents the following innovative research studies:
\u2022 a method for text-independent speaker identification in AmI applications;
\u2022 a method for age estimation from non-ideal samples acquired in AmI scenarios;
\u2022 a privacy-compliant cohort normalization technique to increase the accuracy of already deployed biometric systems;
\u2022 a technology-independent multimodal fusion approach to combine heterogeneous traits in AmI scenarios;
\u2022 a multimodal continuous authentication approach for AmI applications.
The designed novel biometric technologies have been tested on different biometric datasets (both public and collected in our laboratory) simulating the acquisitions performed in AmI applications. Results proved the feasibility of the studied approaches and shown that the studied methods effectively increased the accuracy, applicability, and usability of biometric technologies in AmI with respect to the state-of-the-art