79,968 research outputs found
Distributed Machine Learning for Wireless Communication Networks: Techniques, Architectures, and Applications
Distributed machine learning (DML) techniques, such as federated learning,
partitioned learning, and distributed reinforcement learning, have been
increasingly applied to wireless communications. This is due to improved
capabilities of terminal devices, explosively growing data volume, congestion
in the radio interfaces, and increasing concern of data privacy. The unique
features of wireless systems, such as large scale, geographically dispersed
deployment, user mobility, and massive amount of data, give rise to new
challenges in the design of DML techniques. There is a clear gap in the
existing literature in that the DML techniques are yet to be systematically
reviewed for their applicability to wireless systems. This survey bridges the
gap by providing a contemporary and comprehensive survey of DML techniques with
a focus on wireless networks. Specifically, we review the latest applications
of DML in power control, spectrum management, user association, and edge cloud
computing. The optimality, scalability, convergence rate, computation cost, and
communication overhead of DML are analyzed. We also discuss the potential
adversarial attacks faced by DML applications, and describe state-of-the-art
countermeasures to preserve privacy and security. Last but not least, we point
out a number of key issues yet to be addressed, and collate potentially
interesting and challenging topics for future research
The Convergence of Machine Learning and Communications
The areas of machine learning and communication technology are converging.
Today's communications systems generate a huge amount of traffic data, which
can help to significantly enhance the design and management of networks and
communication components when combined with advanced machine learning methods.
Furthermore, recently developed end-to-end training procedures offer new ways
to jointly optimize the components of a communication system. Also in many
emerging application fields of communication technology, e.g., smart cities or
internet of things, machine learning methods are of central importance. This
paper gives an overview over the use of machine learning in different areas of
communications and discusses two exemplar applications in wireless networking.
Furthermore, it identifies promising future research topics and discusses their
potential impact.Comment: 8 pages, 4 figure
Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and The Way Forward
Connected and autonomous vehicles (CAVs) will form the backbone of future
next-generation intelligent transportation systems (ITS) providing travel
comfort, road safety, along with a number of value-added services. Such a
transformation---which will be fuelled by concomitant advances in technologies
for machine learning (ML) and wireless communications---will enable a future
vehicular ecosystem that is better featured and more efficient. However, there
are lurking security problems related to the use of ML in such a critical
setting where an incorrect ML decision may not only be a nuisance but can lead
to loss of precious lives. In this paper, we present an in-depth overview of
the various challenges associated with the application of ML in vehicular
networks. In addition, we formulate the ML pipeline of CAVs and present various
potential security issues associated with the adoption of ML methods. In
particular, we focus on the perspective of adversarial ML attacks on CAVs and
outline a solution to defend against adversarial attacks in multiple settings
A Survey on the Security of Pervasive Online Social Networks (POSNs)
Pervasive Online Social Networks (POSNs) are the extensions of Online Social
Networks (OSNs) which facilitate connectivity irrespective of the domain and
properties of users. POSNs have been accumulated with the convergence of a
plethora of social networking platforms with a motivation of bridging their
gap. Over the last decade, OSNs have visually perceived an altogether
tremendous amount of advancement in terms of the number of users as well as
technology enablers. A single OSN is the property of an organization, which
ascertains smooth functioning of its accommodations for providing a quality
experience to their users. However, with POSNs, multiple OSNs have coalesced
through communities, circles, or only properties, which make
service-provisioning tedious and arduous to sustain. Especially, challenges
become rigorous when the focus is on the security perspective of cross-platform
OSNs, which are an integral part of POSNs. Thus, it is of utmost paramountcy to
highlight such a requirement and understand the current situation while
discussing the available state-of-the-art. With the modernization of OSNs and
convergence towards POSNs, it is compulsory to understand the impact and reach
of current solutions for enhancing the security of users as well as associated
services. This survey understands this requisite and fixates on different sets
of studies presented over the last few years and surveys them for their
applicability to POSNs...Comment: 39 Pages, 10 Figure
Deep Learning in Information Security
Machine learning has a long tradition of helping to solve complex information
security problems that are difficult to solve manually. Machine learning
techniques learn models from data representations to solve a task. These data
representations are hand-crafted by domain experts. Deep Learning is a
sub-field of machine learning, which uses models that are composed of multiple
layers. Consequently, representations that are used to solve a task are learned
from the data instead of being manually designed.
In this survey, we study the use of DL techniques within the domain of
information security. We systematically reviewed 77 papers and presented them
from a data-centric perspective. This data-centric perspective reflects one of
the most crucial advantages of DL techniques -- domain independence. If
DL-methods succeed to solve problems on a data type in one domain, they most
likely will also succeed on similar data from another domain. Other advantages
of DL methods are unrivaled scalability and efficiency, both regarding the
number of examples that can be analyzed as well as with respect of
dimensionality of the input data. DL methods generally are capable of achieving
high-performance and generalize well.
However, information security is a domain with unique requirements and
challenges. Based on an analysis of our reviewed papers, we point out
shortcomings of DL-methods to those requirements and discuss further research
opportunities
Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence
Along with the rapid developments in communication technologies and the surge
in the use of mobile devices, a brand-new computation paradigm, Edge Computing,
is surging in popularity. Meanwhile, Artificial Intelligence (AI) applications
are thriving with the breakthroughs in deep learning and the many improvements
in hardware architectures. Billions of data bytes, generated at the network
edge, put massive demands on data processing and structural optimization. Thus,
there exists a strong demand to integrate Edge Computing and AI, which gives
birth to Edge Intelligence. In this paper, we divide Edge Intelligence into AI
for edge (Intelligence-enabled Edge Computing) and AI on edge (Artificial
Intelligence on Edge). The former focuses on providing more optimal solutions
to key problems in Edge Computing with the help of popular and effective AI
technologies while the latter studies how to carry out the entire process of
building AI models, i.e., model training and inference, on the edge. This paper
provides insights into this new inter-disciplinary field from a broader
perspective. It discusses the core concepts and the research road-map, which
should provide the necessary background for potential future research
initiatives in Edge Intelligence.Comment: 13 pages, 3 figure
A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications
As the explosive growth of smart devices and the advent of many new
applications, traffic volume has been growing exponentially. The traditional
centralized network architecture cannot accommodate such user demands due to
heavy burden on the backhaul links and long latency. Therefore, new
architectures which bring network functions and contents to the network edge
are proposed, i.e., mobile edge computing and caching. Mobile edge networks
provide cloud computing and caching capabilities at the edge of cellular
networks. In this survey, we make an exhaustive review on the state-of-the-art
research efforts on mobile edge networks. We first give an overview of mobile
edge networks including definition, architecture and advantages. Next, a
comprehensive survey of issues on computing, caching and communication
techniques at the network edge is presented respectively. The applications and
use cases of mobile edge networks are discussed. Subsequently, the key enablers
of mobile edge networks such as cloud technology, SDN/NFV and smart devices are
discussed. Finally, open research challenges and future directions are
presented as well
A Survey on Software-Defined VANETs: Benefits, Challenges, and Future Directions
The evolving of Fifth Generation (5G) networks isbecoming more readily
available as a major driver of the growthof new applications and business
models. Vehicular Ad hocNetworks (VANETs) and Software Defined Networking
(SDN)represent the key enablers of 5G technology with the developmentof next
generation intelligent vehicular networks and applica-tions. In recent years,
researchers have focused on the integrationof SDN and VANET, and look at
different topics related to thearchitecture, the benefits of software-defined
VANET servicesand the new functionalities to adapt them. However, securityand
robustness of the complete architecture is still questionableand have been
largely negleted. Moreover, the deployment andintegration of novel entities and
several architectural componentsdrive new security threats and
vulnerabilities.In this paper, first we survey the state-of-the-art SDN
basedVehicular ad-hoc Network (SDVN) architectures for their net-working
infrastructure design, functionalities, benefits, and chal-lenges. Then we
discuss these SDVN architectures against majorsecurity threats that violate the
key security services such asavailability, confidentiality, authentication, and
data integrity.We also propose different countermeasures to these
threats.Finally, we discuss the lessons learned with the directions offuture
research work towards provisioning stringent security andprivacy solutions in
future SDVN architectures. To the best of ourknowledge, this is the first
comprehensive work that presents sucha survey and analysis on SDVNs in the era
of future generationnetworks (e.g., 5G, and Information centric networking)
andapplications (e.g., intelligent transportation system, and IoT-enabled
advertising in VANETs).Comment: 17 pages, 2 figure
Differential Privacy Techniques for Cyber Physical Systems: A Survey
Modern cyber physical systems (CPSs) has widely being used in our daily lives
because of development of information and communication technologies (ICT).With
the provision of CPSs, the security and privacy threats associated to these
systems are also increasing. Passive attacks are being used by intruders to get
access to private information of CPSs. In order to make CPSs data more secure,
certain privacy preservation strategies such as encryption, and k-anonymity
have been presented in the past. However, with the advances in CPSs
architecture, these techniques also needs certain modifications. Meanwhile,
differential privacy emerged as an efficient technique to protect CPSs data
privacy. In this paper, we present a comprehensive survey of differential
privacy techniques for CPSs. In particular, we survey the application and
implementation of differential privacy in four major applications of CPSs named
as energy systems, transportation systems, healthcare and medical systems, and
industrial Internet of things (IIoT). Furthermore, we present open issues,
challenges, and future research direction for differential privacy techniques
for CPSs. This survey can serve as basis for the development of modern
differential privacy techniques to address various problems and data privacy
scenarios of CPSs.Comment: 46 pages, 12 figure
White Paper on Critical and Massive Machine Type Communication Towards 6G
The society as a whole, and many vertical sectors in particular, is becoming
increasingly digitalized. Machine Type Communication (MTC), encompassing its
massive and critical aspects, and ubiquitous wireless connectivity are among
the main enablers of such digitization at large. The recently introduced 5G New
Radio is natively designed to support both aspects of MTC to promote the
digital transformation of the society. However, it is evident that some of the
more demanding requirements cannot be fully supported by 5G networks.
Alongside, further development of the society towards 2030 will give rise to
new and more stringent requirements on wireless connectivity in general, and
MTC in particular. Driven by the societal trends towards 2030, the next
generation (6G) will be an agile and efficient convergent network serving a set
of diverse service classes and a wide range of key performance indicators
(KPI). This white paper explores the main drivers and requirements of an
MTC-optimized 6G network, and discusses the following six key research
questions:
- Will the main KPIs of 5G continue to be the dominant KPIs in 6G; or will
there emerge new key metrics?
- How to deliver different E2E service mandates with different KPI
requirements considering joint-optimization at the physical up to the
application layer?
- What are the key enablers towards designing ultra-low power receivers and
highly efficient sleep modes?
- How to tackle a disruptive rather than incremental joint design of a
massively scalable waveform and medium access policy for global MTC
connectivity?
- How to support new service classes characterizing mission-critical and
dependable MTC in 6G?
- What are the potential enablers of long term, lightweight and flexible
privacy and security schemes considering MTC device requirements?Comment: White paper by http://www.6GFlagship.co
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