981 research outputs found
Sleep Analytics and Online Selective Anomaly Detection
We introduce a new problem, the Online Selective Anomaly Detection (OSAD), to
model a specific scenario emerging from research in sleep science. Scientists
have segmented sleep into several stages and stage two is characterized by two
patterns (or anomalies) in the EEG time series recorded on sleep subjects.
These two patterns are sleep spindle (SS) and K-complex. The OSAD problem was
introduced to design a residual system, where all anomalies (known and unknown)
are detected but the system only triggers an alarm when non-SS anomalies
appear. The solution of the OSAD problem required us to combine techniques from
both machine learning and control theory. Experiments on data from real
subjects attest to the effectiveness of our approach.Comment: Submitted to 20th ACM SIGKDD Conference on Knowledge Discovery and
Data Mining 201
New Methods for Network Traffic Anomaly Detection
In this thesis we examine the efficacy of applying outlier detection techniques to understand the behaviour of anomalies in communication network traffic. We have identified several shortcomings. Our most finding is that known techniques either focus on characterizing the spatial or temporal behaviour of traffic but rarely both. For example DoS attacks are anomalies which violate temporal patterns while port scans violate the spatial equilibrium of network traffic. To address this observed weakness we have designed a new method for outlier detection based spectral decomposition of the Hankel matrix. The Hankel matrix is spatio-temporal correlation matrix and has been used in many other domains including climate data analysis and econometrics. Using our approach we can seamlessly integrate the discovery of both spatial and temporal anomalies. Comparison with other state of the art methods in the networks community confirms that our approach can discover both DoS and port scan attacks. The spectral decomposition of the Hankel matrix is closely tied to the problem of inference in Linear Dynamical Systems (LDS). We introduce a new problem, the Online Selective Anomaly Detection (OSAD) problem, to model the situation where the objective is to report new anomalies in the system and suppress know faults. For example, in the network setting an operator may be interested in triggering an alarm for malicious attacks but not on faults caused by equipment failure. In order to solve OSAD we combine techniques from machine learning and control theory in a unique fashion. Machine Learning ideas are used to learn the parameters of an underlying data generating system. Control theory techniques are used to model the feedback and modify the residual generated by the data generating state model. Experiments on synthetic and real data sets confirm that the OSAD problem captures a general scenario and tightly integrates machine learning and control theory to solve a practical problem
Personalized data analytics for internet-of-things-based health monitoring
The Internet-of-Things (IoT) has great potential to fundamentally alter the delivery of modern healthcare, enabling healthcare solutions outside the limits of conventional clinical settings. It can offer ubiquitous monitoring to at-risk population groups and allow diagnostic care, preventive care, and early intervention in everyday life. These services can have profound impacts on many aspects of health and well-being. However, this field is still at an infancy stage, and the use of IoT-based systems in real-world healthcare applications introduces new challenges. Healthcare applications necessitate satisfactory quality attributes such as reliability and accuracy due to their mission-critical nature, while at the same time, IoT-based systems mostly operate over constrained shared sensing, communication, and computing resources. There is a need to investigate this synergy between the IoT technologies and healthcare applications from a user-centered perspective. Such a study should examine the role and requirements of IoT-based systems in real-world health monitoring applications. Moreover, conventional computing architecture and data analytic approaches introduced for IoT systems are insufficient when used to target health and well-being purposes, as they are unable to overcome the limitations of IoT systems while fulfilling the needs of healthcare applications. This thesis aims to address these issues by proposing an intelligent use of data and computing resources in IoT-based systems, which can lead to a high-level performance and satisfy the stringent requirements. For this purpose, this thesis first delves into the state-of-the-art IoT-enabled healthcare systems proposed for in-home and in-hospital monitoring. The findings are analyzed and categorized into different domains from a user-centered perspective. The selection of home-based applications is focused on the monitoring of the elderly who require more remote care and support compared to other groups of people. In contrast, the hospital-based applications include the role of existing IoT in patient monitoring and hospital management systems. Then, the objectives and requirements of each domain are investigated and discussed. This thesis proposes personalized data analytic approaches to fulfill the requirements and meet the objectives of IoT-based healthcare systems. In this regard, a new computing architecture is introduced, using computing resources in different layers of IoT to provide a high level of availability and accuracy for healthcare services. This architecture allows the hierarchical partitioning of machine learning algorithms in these systems and enables an adaptive system behavior with respect to the user's condition. In addition, personalized data fusion and modeling techniques are presented, exploiting multivariate and longitudinal data in IoT systems to improve the quality attributes of healthcare applications. First, a real-time missing data resilient decision-making technique is proposed for health monitoring systems. The technique tailors various data resources in IoT systems to accurately estimate health decisions despite missing data in the monitoring. Second, a personalized model is presented, enabling variations and event detection in long-term monitoring systems. The model evaluates the sleep quality of users according to their own historical data. Finally, the performance of the computing architecture and the techniques are evaluated in this thesis using two case studies. The first case study consists of real-time arrhythmia detection in electrocardiography signals collected from patients suffering from cardiovascular diseases. The second case study is continuous maternal health monitoring during pregnancy and postpartum. It includes a real human subject trial carried out with twenty pregnant women for seven months
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
A Robot-Sensor Network Security Architecture for Monitoring Applications
This paper presents SNSR (Sensor Network Security
using Robots), a novel, open, and flexible architecture that
improves security in static sensor networks by benefiting from
robot-sensor network cooperation. In SNSR, the robot performs
sensor node authentication and radio-based localization (enabling
centralized topology computation and route establishment) and
directly interacts with nodes to send them configurations or
receive status and anomaly reports without intermediaries. SNSR
operation is divided into stages set in a feedback iterative
structure, which enables repeating the execution of stages to
adapt to changes, respond to attacks, or detect and correct errors.
By exploiting the robot capabilities, SNSR provides high security
levels and adaptability without requiring complex mechanisms.
This paper presents SNSR, analyzes its security against common
attacks, and experimentally validates its performance
Raising the ClaSS of Streaming Time Series Segmentation
Ubiquitous sensors today emit high frequency streams of numerical
measurements that reflect properties of human, animal, industrial, commercial,
and natural processes. Shifts in such processes, e.g. caused by external events
or internal state changes, manifest as changes in the recorded signals. The
task of streaming time series segmentation (STSS) is to partition the stream
into consecutive variable-sized segments that correspond to states of the
observed processes or entities. The partition operation itself must in
performance be able to cope with the input frequency of the signals. We
introduce ClaSS, a novel, efficient, and highly accurate algorithm for STSS.
ClaSS assesses the homogeneity of potential partitions using self-supervised
time series classification and applies statistical tests to detect significant
change points (CPs). In our experimental evaluation using two large benchmarks
and six real-world data archives, we found ClaSS to be significantly more
precise than eight state-of-the-art competitors. Its space and time complexity
is independent of segment sizes and linear only in the sliding window size. We
also provide ClaSS as a window operator with an average throughput of 538 data
points per second for the Apache Flink streaming engine
- …