IoT-SED: security and efficiency in data transport on Internet of Things

• Grant number: 18/22979-2
• Support Opportunities: Regular Research Grants
• Duration: December 01, 2019 - November 30, 2021
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computer Systems
• Convênio/Acordo: MCTI/MC
• Principal Investigator: Daniel Macêdo Batista
• Grantee: Daniel Macêdo Batista
• Host Institution: Instituto de Matemática e Estatística (IME). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Associated researchers: Gustavo Bittencourt Figueiredo ; Hermes Senger ; Luiz Arthur Feitosa dos Santos ; Rodrigo Campiolo
• Associated scholarship(s): 21/00630-0 - Parallel processing with low-cost computing platforms for analyzing large volumes of security data, BP.TT; 20/08057-5 - Impact of localization of big data processing to detect threats to information security, BP.TT; 20/02639-2 - Methodology for ensuring security in smart city applications, BP.TT; 20/02638-6 - Simulation of transport layer protocols in the Internet of Things, BP.TT

Abstract

Smart cities aim to improve the quality of life of citizens as well as providing a sustainable environment. Since communication between the various components of a smart city is necessary for its operation, a tool on which these cities have been built is the Internet of Things (IoT). Despite the interest in expanding and improving solutions for smart cities and IoT, observed by the investment and by the increasing deployment of globally connected devices, recent studies have shown that these devices do not receive the proper attention when it comes to Information Security. In addition, opportunities for improvement have been observed in communication between nodes of IoT considering the next generation of mobile telecommunication (5G) and in transport layer protocols that are optimized to devices with limited power resources. In that sense, this project presents four research lines related to improvements in various layers of the Internet of Things to create a safe, effective and efficient environment for future applications. This project is related to the theme Internet-Enabler Technologies specified in the call for proposals. (AU)

Scientific publications

(11)

(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)

MOSAIYEBZADEH, FATEMEH; ARAUJO RODRIGUEZ, LUIS GUSTAVO; BATISTA, DANIEL MACEDO; HIRATA JR, R.; VELAZQUEZ, R. A Network Intrusion Detection System using Deep Learning against MQTT Attacks in IoT. 2021 IEEE LATIN-AMERICAN CONFERENCE ON COMMUNICATIONS (LATINCOM 2021), v. N/A, p. 6-pg., 2021-01-01. (15/24485-9, 18/22979-2, 18/23098-0)

ARBEX, GUSTAVO VITRAL; MACHADO, KETLY GONCALVES; NOGUEIRA, MICHELE; BATISTA, DANIEL M.; HIRATA, ROBERTO, JR.; MACHUCA, CM; MARTINS, L; SARGENTO, S; WAUTERS, T; JORGE, L; et al. IoT DDoS Detection Based on Stream Learning. PROCEEDINGS OF THE 2021 12TH INTERNATIONAL CONFERENCE ON NETWORK OF THE FUTURE (NOF 2021), v. N/A, p. 8-pg., 2021-01-01. (14/50937-1, 15/24485-9, 18/22979-2, 18/23098-0)

ARAUJO RODRIGUEZ, LUIS GUSTAVO; BATISTA, DANIEL MACEDO; IEEE. Towards Improving Fuzzer Efficiency for the MQTT Protocol. 26TH IEEE SYMPOSIUM ON COMPUTERS AND COMMUNICATIONS (IEEE ISCC 2021), v. N/A, p. 7-pg., 2021-01-01. (14/50937-1, 18/22979-2, 18/23098-0, 15/24485-9)

PUHL, LUIS; CASSALES, GUILHERME WEIGERT; GUARDIA, HELIO CRESTANA; SENGER, HERMES; GERVASI, O; MURGANTE, B; MISRA, S; GARAU, C; BLECIC, I; TANIAR, D; et al. Distributed Novelty Detection at the Edge for IoT Network Security. COMPUTATIONAL SCIENCE AND ITS APPLICATIONS, ICCSA 2021, PT III, v. 12951, p. 16-pg., 2021-01-01. (15/24461-2, 18/22979-2)

CASSALES, GUILHERME; GOMES, HEITOR; BIFET, ALBERT; PFAHRINGER, BERNHARD; SENGER, HERMES. Improving the performance of bagging ensembles for data streams through mini-batching. INFORMATION SCIENCES, v. 580, p. 260-282, NOV 2021. (18/22979-2, 19/26702-8, 15/24461-2)

COSTA, LOURDES PORTUGAL-POMA; MARCONDES, CESAR A. C.; SENGER, HERMES; GERVASI, O; MURGANTE, B; MISRA, S; GARAU, C; BLECIC, I; TANIAR, D; APDUHAN, BO; et al. Non-cooperative Vehicular Density Prediction in VANETs. COMPUTATIONAL SCIENCE AND ITS APPLICATIONS, ICCSA 2021, PT IV, v. 12952, p. 17-pg., 2021-01-01. (18/22979-2, 15/24461-2)

DOS SANTOS, GIOVANNE MARCELO; BATISTA, DANIEL MACEDO; VELAZQUEZ, R. On-Demand Placement and Scheduling of Virtual Network Functions with Software Requirements. 2020 IEEE LATIN-AMERICAN CONFERENCE ON COMMUNICATIONS (LATINCOM 2020), v. N/A, p. 5-pg., 2020-01-01. (15/24485-9, 14/50937-1, 18/22979-2)

DE OLIVEIRA, GUILHERME WERNECK; NEY, RODRIGO TOSCANO; HERRERA, JUAN LUIS; BATISTA, DANIEL MACEDO; HIRATA, R.; GALAN-JIMENEZ, JAIME; BERROCAL, JAVIER; MURILLO, JUAN MANUEL; DOS SANTOS, ALDRI LUIZ; NOGUEIRA, MICHELE; et al. Predicting Response Time in SDN-Fog Environments for IIoT Applications. 2021 IEEE LATIN-AMERICAN CONFERENCE ON COMMUNICATIONS (LATINCOM 2021), v. N/A, p. 6-pg., 2021-01-01. (14/50937-1, 15/24485-9, 18/22979-2, 18/23098-0)

POVOA, LUCAS VENEZIAN; MARCONDES, CESAR; SENGER, HERMES; MISRA, S; GERVASI, O; MURGANTE, B; STANKOVA, E; KORKHOV, V; TORRE, C; ROCHA, AMAC; et al. Modeling Energy Consumption Based on Resource Utilization. COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2019, PT I, v. 11619, p. 16-pg., 2019-01-01. (18/22979-2, 18/00452-2)

PAIVA, THALES B.; SIQUEIRA, YAISSA; BATISTA, DANIEL MACEDO; HIRATA JR, R.; TERADA, R.; VELAZQUEZ, R. BGP Anomalies Classification using Features based on AS Relationship Graphs. 2021 IEEE LATIN-AMERICAN CONFERENCE ON COMMUNICATIONS (LATINCOM 2021), v. N/A, p. 6-pg., 2021-01-01. (15/24485-9, 18/22979-2, 18/23098-0, 14/50937-1)

BARZILAY, ALAN; MARTINELLI, CAIO L.; NOGUEIRA, MICHELE; BATISTA, DANIEL M.; HIRATA, ROBERTO, JR.; MACHUCA, CM; MARTINS, L; SARGENTO, S; WAUTERS, T; JORGE, L; et al. AnubisFlow: A Feature Extractor for Distributed Denial of Service Attack Classification. PROCEEDINGS OF THE 2021 12TH INTERNATIONAL CONFERENCE ON NETWORK OF THE FUTURE (NOF 2021), v. N/A, p. 8-pg., 2021-01-01. (15/24485-9, 18/22979-2, 18/23098-0, 14/50937-1)

Filed patent(s) as a result of this research project