20 research outputs found

    Unsupervised tracking of time-evolving data streams and an application to short-term urban traffic flow forecasting

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    I am indebted to many people for their help and support I receive during my Ph.D. study and research at DIBRIS-University of Genoa. First and foremost, I would like to express my sincere thanks to my supervisors Prof.Dr. Masulli, and Prof.Dr. Rovetta for the invaluable guidance, frequent meetings, and discussions, and the encouragement and support on my way of research. I thanks all the members of the DIBRIS for their support and kindness during my 4 years Ph.D. I would like also to acknowledge the contribution of the projects Piattaforma per la mobili\ue0 Urbana con Gestione delle INformazioni da sorgenti eterogenee (PLUG-IN) and COST Action IC1406 High Performance Modelling and Simulation for Big Data Applications (cHiPSet). Last and most importantly, I wish to thanks my family: my wife Shaimaa who stays with me through the joys and pains; my daughter and son whom gives me happiness every-day; and my parents for their constant love and encouragement

    Intelligent Sensor Networks

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    In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts

    Novel neural approaches to data topology analysis and telemedicine

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    1noL'abstract è presente nell'allegato / the abstract is in the attachmentopen676. INGEGNERIA ELETTRICAnoopenRandazzo, Vincenz

    db-outlier detection algorithm using divide and conquer approach over dynamic datastream

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    Anomaly detection is currently an important and active research problem in many fields and involved in numerous applications. Most of the existing methods are based on distance measure which can produce better results as compared to other methods. But i

    Proceedings of the ECMLPKDD 2015 Doctoral Consortium

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    ECMLPKDD 2015 Doctoral Consortium was organized for the second time as part of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD), organised in Porto during September 7-11, 2015. The objective of the doctoral consortium is to provide an environment for students to exchange their ideas and experiences with peers in an interactive atmosphere and to get constructive feedback from senior researchers in machine learning, data mining, and related areas. These proceedings collect together and document all the contributions of the ECMLPKDD 2015 Doctoral Consortium

    Optimized and Automated Machine Learning Techniques Towards IoT Data Analytics and Cybersecurity

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    The Internet-of-Things (IoT) systems have emerged as a prevalent technology in our daily lives. With the wide spread of sensors and smart devices in recent years, the data generation volume and speed of IoT systems have increased dramatically. In most IoT systems, massive volumes of data must be processed, transformed, and analyzed on a frequent basis to enable various IoT services and functionalities. Machine Learning (ML) approaches have shown their capacity for IoT data analytics. However, applying ML models to IoT data analytics tasks still faces many difficulties and challenges. The first challenge is to process large amounts of dynamic IoT data to make accurate and informed decisions. The second challenge is to automate and optimize the data analytics process. The third challenge is to protect IoT devices and systems against various cyber threats and attacks. To address the IoT data analytics challenges, this thesis proposes various ML-based frameworks and data analytics approaches in several applications. Specifically, the first part of the thesis provides a comprehensive review of applying Automated Machine Learning (AutoML) techniques to IoT data analytics tasks. It discusses all procedures of the general ML pipeline. The second part of the thesis proposes several supervised ML-based novel Intrusion Detection Systems (IDSs) to improve the security of the Internet of Vehicles (IoV) systems and connected vehicles. Optimization techniques are used to obtain optimized ML models with high attack detection accuracy. The third part of the thesis developed unsupervised ML algorithms to identify network anomalies and malicious network entities (e.g., attacker IPs, compromised machines, and polluted files/content) to protect Content Delivery Networks (CDNs) from service targeting attacks, including distributed denial of service and cache pollution attacks. The proposed framework is evaluated on real-world CDN access log data to illustrate its effectiveness. The fourth part of the thesis proposes adaptive online learning algorithms for addressing concept drift issues (i.e., data distribution changes) and effectively handling dynamic IoT data streams in order to provide reliable IoT services. The development of drift adaptive learning methods can effectively adapt to data distribution changes and avoid data analytics model performance degradation

    Developing Event Identification Methods for Structured and Unstructured Data Streams

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    Data, now more than ever before, are continuously being generated in huge volumes, andat rapid speed. Data may originate from various sources, for instance: sensor readings,financial transactions, social networks, etc.. A data stream is a continuous sequence ofdata arriving in almost real-time and often at a high speed. In this thesis, we are interested in benefiting from the availability of such data anddeveloping methods for detecting the occurrence of events from data streams, such as atext stream and a price time-series stream. Hence, we have explored event identificationfrom structured and unstructured data streams in the domain of finance. We employ the Directional Change (DC) approach to high frequency time-seriesstreams to identify significant price transitions (i.e. events). DC is an event-basedapproach for summarizing price movements based on a fixed, a-priori threshold. Wepropose a dynamic threshold definition method, which replaces the fixed threshold andis appropriate for markets that operate over specific opening and closing times. Adynamic threshold provides more flexibility and extends the DC approach allowing theidentification of price changes in continuously changing environments. With the proliferation of social media data reporting on all aspects of human activ-ity, being able to automatically identify events is becoming increasingly important. Wepresent a framework for detecting the occurring events on a daily basis, via social net-work streams. We develop and extend a Frequent Pattern Mining method by proposinga dynamic support definition method to replace the fixed support. As the number oftext posts streamed each day is not fixed, a dynamic support, can adapt to the natureof data streams and can improve the identification of events. Finally, we explore whether we can bring together the insights from the time-seriesstream and the social network stream to understand if events as identified from bothstreams can be correlated

    LIPIcs, Volume 244, ESA 2022, Complete Volume

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    LIPIcs, Volume 244, ESA 2022, Complete Volum

    Computational Methods for Medical and Cyber Security

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    Over the past decade, computational methods, including machine learning (ML) and deep learning (DL), have been exponentially growing in their development of solutions in various domains, especially medicine, cybersecurity, finance, and education. While these applications of machine learning algorithms have been proven beneficial in various fields, many shortcomings have also been highlighted, such as the lack of benchmark datasets, the inability to learn from small datasets, the cost of architecture, adversarial attacks, and imbalanced datasets. On the other hand, new and emerging algorithms, such as deep learning, one-shot learning, continuous learning, and generative adversarial networks, have successfully solved various tasks in these fields. Therefore, applying these new methods to life-critical missions is crucial, as is measuring these less-traditional algorithms' success when used in these fields

    Eight Biennial Report : April 2005 – March 2007

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