3,789 research outputs found
Anomaly detection mechanisms to find social events using cellular traffic data
The design of new tools to detect on-the-fly traffic anomaly without scalability problems is a key point to exploit the cellular system for monitoring social activities. To this goal, the paper proposes two methods based on the wavelet analysis of the cumulative cellular traffic. The utilisation of the wavelets permits to easily filter “normal” traffic anomalies such as the periodic trends present in the cellular traffic. The two presented approaches, denoted as Spatial Analysis (SA) and Time Analysis (TA), differ on how they consider the spatial information of the traffic data. We examine the performance of the considered algorithms using cellular traffic data acquired from one the most important Italian Mobile Network Operator in the city of Milan throughout December 2013. The results highlight the weak points of TA and some important features of SA. Both approaches overcome the performance of one reference algorithm present in literature. The strategy used in the SA emerges as the most suitable for exploiting the spatial correlation when we aim at the detection of the traffic anomaly focused on the localisation of social events
A survey of machine learning techniques applied to self organizing cellular networks
In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future
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
AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments
This report considers the application of Articial Intelligence (AI) techniques to
the problem of misuse detection and misuse localisation within telecommunications
environments. A broad survey of techniques is provided, that covers inter alia
rule based systems, model-based systems, case based reasoning, pattern matching,
clustering and feature extraction, articial neural networks, genetic algorithms, arti
cial immune systems, agent based systems, data mining and a variety of hybrid
approaches. The report then considers the central issue of event correlation, that
is at the heart of many misuse detection and localisation systems. The notion of
being able to infer misuse by the correlation of individual temporally distributed
events within a multiple data stream environment is explored, and a range of techniques,
covering model based approaches, `programmed' AI and machine learning
paradigms. It is found that, in general, correlation is best achieved via rule based approaches,
but that these suffer from a number of drawbacks, such as the difculty of
developing and maintaining an appropriate knowledge base, and the lack of ability
to generalise from known misuses to new unseen misuses. Two distinct approaches
are evident. One attempts to encode knowledge of known misuses, typically within
rules, and use this to screen events. This approach cannot generally detect misuses
for which it has not been programmed, i.e. it is prone to issuing false negatives.
The other attempts to `learn' the features of event patterns that constitute normal
behaviour, and, by observing patterns that do not match expected behaviour, detect
when a misuse has occurred. This approach is prone to issuing false positives,
i.e. inferring misuse from innocent patterns of behaviour that the system was not
trained to recognise. Contemporary approaches are seen to favour hybridisation,
often combining detection or localisation mechanisms for both abnormal and normal
behaviour, the former to capture known cases of misuse, the latter to capture
unknown cases. In some systems, these mechanisms even work together to update
each other to increase detection rates and lower false positive rates. It is concluded
that hybridisation offers the most promising future direction, but that a rule or state
based component is likely to remain, being the most natural approach to the correlation
of complex events. The challenge, then, is to mitigate the weaknesses of
canonical programmed systems such that learning, generalisation and adaptation
are more readily facilitated
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
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