Network communication privacy: traffic masking against traffic analysis

An increasing number of recent experimental works have been demonstrating the supposedly secure channels in the Internet are prone to privacy breaking under many respects, due to traffic features leaking information on the user activity and traffic content. As a matter of example, traffic flow classification at application level, web page identification, language/phrase detection in VoIP communications have all been successfully demonstrated against encrypted channels. In this thesis I aim at understanding if and how complex it is to obfuscate the information leaked by traffic features, namely packet lengths, direction, times. I define a security model that points out what the ideal target of masking is, and then define the optimized and practically implementable masking algorithms, yielding a trade-off between privacy and overhead/complexity of the masking algorithm. Numerical results are based on measured Internet traffic traces. Major findings are that: i) optimized full masking achieves similar overhead values with padding only and in case fragmentation is allowed; ii) if practical realizability is accounted for, optimized statistical masking algorithms attain only moderately better overhead than simple fixed pattern masking algorithms, while still leaking correlation information that can be exploited by the adversary

Global Warming Estimation from MSU

In this study, we have developed time series of global temperature from 1980-97 based on the Microwave Sounding Unit (MSU) Ch 2 (53.74 GHz) observations taken from polar-orbiting NOAA operational satellites. In order to create these time series, systematic errors (approx. 0.1 K) in the Ch 2 data arising from inter-satellite differences are removed objectively. On the other hand, smaller systematic errors (approx. 0.03 K) in the data due to orbital drift of each satellite cannot be removed objectively. Such errors are expected to remain in the time series and leave an uncertainty in the inferred global temperature trend. With the help of a statistical method, the error in the MSU inferred global temperature trend resulting from orbital drifts and residual inter-satellite differences of all satellites is estimated to be 0.06 K decade. Incorporating this error, our analysis shows that the global temperature increased at a rate of 0.13 +/- 0.06 K decade during 1980-97

Adversarial Attacks on Remote User Authentication Using Behavioural Mouse Dynamics

Mouse dynamics is a potential means of authenticating users. Typically, the authentication process is based on classical machine learning techniques, but recently, deep learning techniques have been introduced for this purpose. Although prior research has demonstrated how machine learning and deep learning algorithms can be bypassed by carefully crafted adversarial samples, there has been very little research performed on the topic of behavioural biometrics in the adversarial domain. In an attempt to address this gap, we built a set of attacks, which are applications of several generative approaches, to construct adversarial mouse trajectories that bypass authentication models. These generated mouse sequences will serve as the adversarial samples in the context of our experiments. We also present an analysis of the attack approaches we explored, explaining their limitations. In contrast to previous work, we consider the attacks in a more realistic and challenging setting in which an attacker has access to recorded user data but does not have access to the authentication model or its outputs. We explore three different attack strategies: 1) statistics-based, 2) imitation-based, and 3) surrogate-based; we show that they are able to evade the functionality of the authentication models, thereby impacting their robustness adversely. We show that imitation-based attacks often perform better than surrogate-based attacks, unless, however, the attacker can guess the architecture of the authentication model. In such cases, we propose a potential detection mechanism against surrogate-based attacks.Comment: Accepted in 2019 International Joint Conference on Neural Networks (IJCNN). Update of DO

Enrique CAMARA DE LANDA : Etnomusicologia

On aurait pu penser qu’avec le livre Etnomusicología, publié en Espagne en 2003, nous avions désormais un manuel complet d’introduction à l’ethnomusicologie touchant les aspects majeurs de la discipline (histoire, méthodes, terrains, champs d’investigations, problématiques, analyses, méthodes). Parue en 2014, cette nouvelle édition italienne, augmentée et enrichie de chapitres inédits offre pourtant de substantielles améliorations et de précieux compléments. Etnomusicologia – titre italien de..

Rain retrieval method for mesoscale convective systems

The analysis of recent high-resolution aircraft observations over the ocean made by radar and passive microwave radiometer reveals significant problems in relating the brightness temperature measurements of the radiometer with the radar-derived rain rates. A predominant cause of this problem is that the information on rain drops contained in the radiometric measurements is contaminated by scattering and emission from other hydrometeors present in the field of view (fov) of the radiometer. Extensive observations of rain rate made by ship-borne radars and by the multichannel Special Sensor Microwave Imager (SSM/I), with a much larger fov, lead to similar conclusions. Considering the variability in the meteorological conditions, and in the hydrometeors spatial distribution, we developed an empirical method to estimate rain rate based on two parameters derived from the SSM/I data, which are related to the convective dynamics. The calibration of this empirical algorithm was performed with radar ground truth for November 1992, available over the TOGA-COARE (Tropical Ocean Global Atmosphere-Coupled Ocean Atmosphere Response Experiment) region. Then the algorithm was applied to the same TOGA-COARE region for the remaining three months available. The comparison between the estimated rain rate and the radar observations gives a correlation coefficient of about 0.85, and the monthly total estimated rainfall has an error of about 13%. This rain retrieval method, tuned for Mesoscale Convective Systems (MCSs), is applicable to the Tropical Rain Measuring Mission (TRMM), where microwave radiometric observations and simultaneous radar observations are available

Real Time Identification of SSH Encrypted Application Flows by Using Cluster Analysis Techniques

Abstract. The identification of application flows is a critical task in order to manage bandwidth requirements of different kind of services (i.e. VOIP, Video, ERP). As network security functions spread, an increasing amount of traffic is natively encrypted due to privacy issues (e.g. VPN). This makes ineffective current traffic classification systems based on ports and payload inspection, e.g. even powerful Deep Packet Inspection is useless to classify application flow carried inside SSH sessions. We have developed a real time traffic classification method based on cluster analysis to identify SSH flows from statistical behavior of IP traffic parameters, such as length, arrival times and direction of packets. In this paper we describe our approach and relevant obtained results. We achieve detection rate up to 99.5 % in classifying SSH flows and accuracy up to 99.88 % for application flows carried within those flows, such as SCP, SFTP and HTTP over SSH

Imported Chikungunya Infection, Italy

From July to September 2006, a total of 17 confirmed cases of CHIKV infection were observed in travelers at 5 Gruppo di Interesse e Studio delle Patologie di Importazione (GISPI) centers (Italian network of Institutes of Infectious and Tropical Diseases). Prompt reporting of imported CHIKV infections is essential for monitoring of potential risk. The possibility of introducing CHIKV into Italy cannot be ruled out on the basis of current evidence