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Altair Olivo Santin
Researcher at Pontifícia Universidade Católica do Paraná
Publications - 78
Citations - 920
Altair Olivo Santin is an academic researcher from Pontifícia Universidade Católica do Paraná. The author has contributed to research in topics: Intrusion detection system & Computer science. The author has an hindex of 13, co-authored 70 publications receiving 675 citations. Previous affiliations of Altair Olivo Santin include Universidade Federal de Santa Catarina.
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Toward a reliable anomaly-based intrusion detection in real-world environments
TL;DR: A new method for creating intrusion databases that is easy to update and reproduce with real and valid traffic, representative, and publicly available is presented and the results show that most of the assumptions frequently applied in studies in the literature do not hold when using a machine learning detection scheme for network-based intrusion detection.
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Towards an Energy-Efficient Anomaly-Based Intrusion Detection Engine for Embedded Systems
Eduardo Viegas,Altair Olivo Santin,André Luiz Pereira de França,Ricardo P. Jasinski,Volnei A. Pedroni,Luiz S. Oliveira +5 more
TL;DR: It is demonstrated that a hardware (HW) implementation of network security algorithms can significantly reduce their energy consumption compared to an equivalent software (SW) version.
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BigFlow: Real-time and reliable anomaly-based intrusion detection for high-speed networks
TL;DR: BigFlow is an approach capable of processing evolving network traffic while being scalable to large packet rates, and employs a verification method that checks if the classifier outcome is valid in order to provide reliability.
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A Three-Ballot-Based Secure Electronic Voting System
TL;DR: A secure electronic voting system integrated in a single architecture-one that addresses vote receipts, uniqueness and materialization of the vote, and voter privacy and anonymity is presented.
Proceedings ArticleDOI
Octopus-IIDS: An anomaly based intelligent intrusion detection system
TL;DR: An intrusion detection system model based on the behavior of network traffic through the analysis and classification of messages is presented, and two artificial intelligence techniques named Kohonen neural network and support vector machine are applied to detect anomalies.