3,485 research outputs found
Big Data in Critical Infrastructures Security Monitoring: Challenges and Opportunities
Critical Infrastructures (CIs), such as smart power grids, transport systems,
and financial infrastructures, are more and more vulnerable to cyber threats,
due to the adoption of commodity computing facilities. Despite the use of
several monitoring tools, recent attacks have proven that current defensive
mechanisms for CIs are not effective enough against most advanced threats. In
this paper we explore the idea of a framework leveraging multiple data sources
to improve protection capabilities of CIs. Challenges and opportunities are
discussed along three main research directions: i) use of distinct and
heterogeneous data sources, ii) monitoring with adaptive granularity, and iii)
attack modeling and runtime combination of multiple data analysis techniques.Comment: EDCC-2014, BIG4CIP-201
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
Damage classification and estimation in experimental structures using time series analysis and pattern recognition
Peer reviewedPreprin
Automatic error detection using program invariants for fault localization
Tese de Mestrado Integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 201
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