Spectral analysis to anomaly detection in dynamic attributed graphs

• Grant number: 17/24185-0
• Support Opportunities: Scholarships in Brazil - Doctorate (Direct)
• Start date: July 01, 2018
• End date: May 23, 2021
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Mariá Cristina Vasconcelos Nascimento Rosset
• Grantee: Rodrigo Francisquini da Silva
• Host Institution: Instituto de Ciência e Tecnologia (ICT). Universidade Federal de São Paulo (UNIFESP). Campus São José dos Campos. São José dos Campos , SP, Brazil
• Associated research grant: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry, AP.CEPID

Abstract

Anomaly detection in data has a wide variety of applications, such as intrusion detection in computer networks or credit card fraud detection. In particular, if the data can represented by graphs, metrics and methods based on graph theory known for high quality solutions can be used. In the case of static graphs, the anomaly detection problem has been extensively studied and several algorithms have been proposed. However, there are a few studies that aim at anomaly detection in dynamic attributed networks. In these cases, the majority of the existing strategies considers node and edge updates, ignoring the history of the changes along the anomaly detection process. In addition, few strategies are scalable to deal with Big Data. In this case, clustering-based anomaly detection strategies are pointed out as a good solution, since they allow to analyze groups of vertices instead of individual vertices and, therefore, have a lower computational cost. Thereby, this project aims atinvestigating the existing unsupervised methods for anomaly detection in dynamic attributed networks. As main contribution, a scalable strategy for anomaly detection in dynamic attributed networks will be proposed. This strategy will use a clustering algorithm and spectral operators that will also be developed during this project. The developed strategy will be tested to attest its efficiency and its results will be compared with those from the best strategies found in the literature. (AU)

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)

SILVA, TIAGO TIBURCIO DA; FRANCISQUINI, RODRIGO; NASCIMENTO, MARIA C. V.. Meteorological and human mobility data on predicting COVID-19 cases by a novel hybrid decomposition method with anomaly detection analysis: A case study in the capitals of Brazil. EXPERT SYSTEMS WITH APPLICATIONS, v. 182, NOV 15 2021. (13/07375-0, 17/24185-0)

FRANCISQUINI, RODRIGO; DA SILVA, TIAGO T.; NASCIMENTO, MARIA C., V; IEEE. Detecting Anomalies In Daily COVID-19 Cases Data From Brazil Capitals Using GSP Theory. 2021 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC 2021), v. N/A, p. 8-pg., 2021-01-01. (13/07375-0, 17/24185-0)

FRANCISQUINI, RODRIGO; BERTON, RAFAEL; SOARES, SANDRO GOMES; PESSOTTI, DAYELLE S.; CAMACHO, MAURICIO F.; ANDRADE-SILVA, DEBORA; BARCICK, UILLA; SERRANO, SOLANGE M. T.; CHAMMAS, ROGER; NASCIMENTO, MARIA C. V.; et al. Community-based network analyses reveal emerging connectivity patterns of protein-protein interactions in murine melanoma secretome. JOURNAL OF PROTEOMICS, v. 232, FEB 10 2021. (17/22330-3, 15/21660-4, 13/07375-0, 17/24185-0, 14/06579-3, 19/10817-0, 13/07467-1)

FRANCISQUINI, RODRIGO; LORENA, ANA CAROLINA; NASCIMENTO, MARIA C., V. Community-based anomaly detection using spectral graph filtering. APPLIED SOFT COMPUTING, v. 118, p. 13-pg., 2022-03-01. (17/24185-0)