9,037 research outputs found
NEMESYS: Enhanced Network Security for Seamless Service Provisioning in the Smart Mobile Ecosystem
As a consequence of the growing popularity of smart mobile devices, mobile
malware is clearly on the rise, with attackers targeting valuable user
information and exploiting vulnerabilities of the mobile ecosystems. With the
emergence of large-scale mobile botnets, smartphones can also be used to launch
attacks on mobile networks. The NEMESYS project will develop novel security
technologies for seamless service provisioning in the smart mobile ecosystem,
and improve mobile network security through better understanding of the threat
landscape. NEMESYS will gather and analyze information about the nature of
cyber-attacks targeting mobile users and the mobile network so that appropriate
counter-measures can be taken. We will develop a data collection infrastructure
that incorporates virtualized mobile honeypots and a honeyclient, to gather,
detect and provide early warning of mobile attacks and better understand the
modus operandi of cyber-criminals that target mobile devices. By correlating
the extracted information with the known patterns of attacks from wireline
networks, we will reveal and identify trends in the way that cyber-criminals
launch attacks against mobile devices.Comment: Accepted for publication in Proceedings of the 28th International
Symposium on Computer and Information Sciences (ISCIS'13); 9 pages; 1 figur
The Dark Side(-Channel) of Mobile Devices: A Survey on Network Traffic Analysis
In recent years, mobile devices (e.g., smartphones and tablets) have met an
increasing commercial success and have become a fundamental element of the
everyday life for billions of people all around the world. Mobile devices are
used not only for traditional communication activities (e.g., voice calls and
messages) but also for more advanced tasks made possible by an enormous amount
of multi-purpose applications (e.g., finance, gaming, and shopping). As a
result, those devices generate a significant network traffic (a consistent part
of the overall Internet traffic). For this reason, the research community has
been investigating security and privacy issues that are related to the network
traffic generated by mobile devices, which could be analyzed to obtain
information useful for a variety of goals (ranging from device security and
network optimization, to fine-grained user profiling).
In this paper, we review the works that contributed to the state of the art
of network traffic analysis targeting mobile devices. In particular, we present
a systematic classification of the works in the literature according to three
criteria: (i) the goal of the analysis; (ii) the point where the network
traffic is captured; and (iii) the targeted mobile platforms. In this survey,
we consider points of capturing such as Wi-Fi Access Points, software
simulation, and inside real mobile devices or emulators. For the surveyed
works, we review and compare analysis techniques, validation methods, and
achieved results. We also discuss possible countermeasures, challenges and
possible directions for future research on mobile traffic analysis and other
emerging domains (e.g., Internet of Things). We believe our survey will be a
reference work for researchers and practitioners in this research field.Comment: 55 page
Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence
IEEE Access
Volume 3, 2015, Article number 7217798, Pages 1512-1530
Open Access
Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article)
Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc
a Department of Information Engineering, University of Padua, Padua, Italy
b Department of General Psychology, University of Padua, Padua, Italy
c IRCCS San Camillo Foundation, Venice-Lido, Italy
View additional affiliations
View references (107)
Abstract
In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network
An Empirical Air-to-Ground Channel Model Based on Passive Measurements in LTE
In this paper, a recently conducted measurement campaign for
unmanned-aerial-vehicle (UAV) channels is introduced. The downlink signals of
an in-service long-time-evolution (LTE) network which is deployed in a suburban
scenario were acquired. Five horizontal and five vertical flight routes were
considered. The channel impulse responses (CIRs) are extracted from the
received data by exploiting the cell specific signals (CRSs). Based on the
CIRs, the parameters of multipath components (MPCs) are estimated by using a
high-resolution algorithm derived according to the space-alternating
generalized expectation-maximization (SAGE) principle. Based on the SAGE
results, channel characteristics including the path loss, shadow fading, fast
fading, delay spread and Doppler frequency spread are thoroughly investigated
for different heights and horizontal distances, which constitute a stochastic
model.Comment: 15 pages, submitted version to IEEE Transactions on Vehicular
Technology. Current status: Early acces
Electromagnetic field assessment as a smart city service: The SmartSantander use-case
Despite the increasing presence of wireless communications in everyday life, there exist some voices raising concerns about their adverse effects. One particularly relevant example is the potential impact of the electromagnetic field they induce on the population's health. Traditionally, very specialized methods and devices (dosimetry) have been used to assess the strength of the E-field, with the main objective of checking whether it respects the corresponding regulations. In this paper, we propose a complete novel approach, which exploits the functionality leveraged by a smart city platform. We deploy a number of measuring probes, integrated as sensing devices, to carry out a characterization embracing large areas, as well as long periods of time. This unique platform has been active for more than one year, generating a vast amount of information. We process such information, and the obtained results validate the whole methodology. In addition, we discuss the variation of the E-field caused by cellular networks, considering additional information, such as usage statistics. Finally, we establish the exposure that can be attributed to the base stations within the scenario under analysis.This work has been supported by the Spanish Government (Ministerio de EconomÃa y Competitividad, Fondo Europeo de Desarrollo Regional, FEDER) by means of the project ADVICE: Dynamic provisioning of connectivity in high density 5G wireless scenarios (TEC2015-71329-C2-1-R)
Distributions of Human Exposure to Ozone During Commuting Hours in Connecticut using the Cellular Device Network
Epidemiologic studies have established associations between various air
pollutants and adverse health outcomes for adults and children. Due to high
costs of monitoring air pollutant concentrations for subjects enrolled in a
study, statisticians predict exposure concentrations from spatial models that
are developed using concentrations monitored at a few sites. In the absence of
detailed information on when and where subjects move during the study window,
researchers typically assume that the subjects spend their entire day at home,
school or work. This assumption can potentially lead to large exposure
assignment bias. In this study, we aim to determine the distribution of the
exposure assignment bias for an air pollutant (ozone) when subjects are assumed
to be static as compared to accounting for individual mobility. To achieve this
goal, we use cell-phone mobility data on approximately 400,000 users in the
state of Connecticut during a week in July, 2016, in conjunction with an ozone
pollution model, and compare individual ozone exposure assuming static versus
mobile scenarios. Our results show that exposure models not taking mobility
into account often provide poor estimates of individuals commuting into and out
of urban areas: the average 8-hour maximum difference between these estimates
can exceed 80 parts per billion (ppb). However, for most of the population, the
difference in exposure assignment between the two models is small, thereby
validating many current epidemiologic studies focusing on exposure to ozone
- …