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Stream project: security in real-time with elasticity, analytic, and monitoring

Grant number: 15/24514-9
Support type:Regular Research Grants
Duration: October 01, 2017 - April 30, 2020
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Cooperation agreement: MCTI/MC
Principal researcher:Otto Carlos Muniz Bandeira Duarte
Grantee:Otto Carlos Muniz Bandeira Duarte
Home Institution: Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa (COPPE). Universidade Federal do Rio de Janeiro (UFRJ). Ministério da Educação (Brasil)
Assoc. researchers: Anelise Munaretto Fonseca ; Mauro Sérgio Pereira Fonseca

Abstract

Most of current threats are detected long after they occurred, considerably increasing the risk of irreparable damages, and disabling any defense attempt. The late detection of those threats is a consequence of the high complexity of the attacks, more specialized every day, and the huge amount of data (Big Data) to be analyzed and monitored by security specialists. Although very hard analyze, attacks always leave traces, or trails, that can be detected with machine learning techniques through real-time stream processing. The efficiency of the defense mechanisms requires reducing the detection time of threats, from months to minutes or hours. Therefore, the STREAM project focuses on collecting, enriching data, and processing data in real-time to detect security threats. We propose to develop a platform to promptly detect security threat and start an immediate defense of the target. The project propose the development of a platform for real time threat detection, which is based in open source tools and released to the community. The services provided by the proposed platform ensure system security for both known and unknown attacks through various automated machine learning methods of attack classification and network anomaly detection. (AU)

Articles published in Agência FAPESP Newsletter about the research grant:
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Scientific publications (4)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
TORRES, JOAO VITOR; ALVARENGA, IGOR DRUMMOND; BOUTABA, RAOUF; MUNIZ BANDEIRA DUARTE, OTTO CARLOS. Evaluating CRoS-NDN: a comparative performance analysis of a controller-based routing scheme for named-data networking. JOURNAL OF INTERNET SERVICES AND APPLICATIONS, v. 10, n. 1 DEC 2019. Web of Science Citations: 0.
LOPEZ, MARTIN ANDREONI; MATTOS, DIOGO M. F.; DUARTE, OTTO CARLOS M. B.; PUJOLLE, GUY. A fast unsupervised preprocessing method for network monitoring. ANNALS OF TELECOMMUNICATIONS, v. 74, n. 3-4, SI, p. 139-155, APR 2019. Web of Science Citations: 0.
FERRAZANI MATTOS, DIOGO MENEZES; VELLOSO, PEDRO BRACONNOT; MUNIZ BANDEIRA DUARTE, OTTO CARLOS. An agile and effective network function virtualization infrastructure for the Internet of Things. JOURNAL OF INTERNET SERVICES AND APPLICATIONS, v. 10, MAR 15 2019. Web of Science Citations: 2.
FERRAZANI MATTOS, DIOGO MENEZES; MUNIZ BANDEIRA DUARTE, OTTO CARLOS; PUJOLLE, GUY. A lightweight protocol for consistent policy update on software-defined networking with multiple controllers. JOURNAL OF NETWORK AND COMPUTER APPLICATIONS, v. 122, p. 77-87, NOV 15 2018. Web of Science Citations: 1.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.