5,724 research outputs found
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
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
Bridges Structural Health Monitoring and Deterioration Detection Synthesis of Knowledge and Technology
INE/AUTC 10.0
Towards a Sustainable City for Cyclists: Promoting Safety through a Mobile Sensing Application
[EN] Riding a bicycle is a great manner to contribute to the preservation of our ecosystem. Cycling helps to reduce air pollution and traffic congestion, and so, it is one of the simplest ways to lower the environmental footprint of people. However, the cohabitation of cars and vulnerable road users, such as bikes, scooters, or pedestrians, is prone to cause accidents with serious consequences. In this context, technological solutions are sought that enable the generation of alerts to prevent these accidents, thereby promoting a safer city for these road users, and a cleaner environment. Alert systems based on smartphones can alleviate these situations since nearly all people carry such a device while traveling. In this work, we test the suitability of a smartphone based alert system, determining the most adequate communications architecture. Two protocols have been designed to send position and alert messages to/from a centralized server over 4G cellular networks. One of the protocols is implemented using a REST architecture on top of the HTTP protocol, and the other one is implemented over the UDP protocol. We show that the proposed alarm system is feasible regarding communication response time, and we conclude that the application should be implemented over the UDP protocol, as response times are about three times better than for the REST implementation. We tested the applications in real deployments, finding that drivers are warned of the presence of bicycles when closer than 150 m, having enough time to pay attention to the situation and drive more carefully to avoid a collision.This work was partially supported by the "Ministerio de Ciencia, Innovacion y Universidades, Programa Estatal de Investigacion, Desarrollo e Innovacion Orientada a los Retos de la Sociedad, Proyectos I+D+I 2018", Spain, under Grant RTI2018-096384-B-I00.Boronat, P.; PĂ©rez-Francisco, M.; Tavares De Araujo Cesariny Calafate, CM.; Cano, J. (2021). Towards a Sustainable City for Cyclists: Promoting Safety through a Mobile Sensing Application. Sensors. 21(6):1-18. https://doi.org/10.3390/s2106211611821
A Supervised Embedding and Clustering Anomaly Detection method for classification of Mobile Network Faults
The paper introduces Supervised Embedding and Clustering Anomaly Detection
(SEMC-AD), a method designed to efficiently identify faulty alarm logs in a
mobile network and alleviate the challenges of manual monitoring caused by the
growing volume of alarm logs. SEMC-AD employs a supervised embedding approach
based on deep neural networks, utilizing historical alarm logs and their labels
to extract numerical representations for each log, effectively addressing the
issue of imbalanced classification due to a small proportion of anomalies in
the dataset without employing one-hot encoding. The robustness of the embedding
is evaluated by plotting the two most significant principle components of the
embedded alarm logs, revealing that anomalies form distinct clusters with
similar embeddings. Multivariate normal Gaussian clustering is then applied to
these components, identifying clusters with a high ratio of anomalies to normal
alarms (above 90%) and labeling them as the anomaly group. To classify new
alarm logs, we check if their embedded vectors' two most significant principle
components fall within the anomaly-labeled clusters. If so, the log is
classified as an anomaly. Performance evaluation demonstrates that SEMC-AD
outperforms conventional random forest and gradient boosting methods without
embedding. SEMC-AD achieves 99% anomaly detection, whereas random forest and
XGBoost only detect 86% and 81% of anomalies, respectively. While supervised
classification methods may excel in labeled datasets, the results demonstrate
that SEMC-AD is more efficient in classifying anomalies in datasets with
numerous categorical features, significantly enhancing anomaly detection,
reducing operator burden, and improving network maintenance
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