Research Grants 21/06733-6 - Redes de computadores, Inteligência artificial - BV FAPESP
Advanced search
Start date
Betweenand

Identifying and obfuscating security vulnerabilities and behaviors in IoT

Abstract

This project aims at preventing data leaks in the transmission of network messages. Data privacy is increasingly necessary given the advent of IoT, which generates sensitive data, and the implementation of the Brazilian General Data Protection Law (LGPD). In academia, side-channel attacks are diligently investigated, which only by observing network traffic and by means of statistical and Artificial Intelligence methods to infer patterns and behaviors that reveal sensitive information to users, compromising their privacy. Thus, this project seeks to model information leakage in IoT and propose effective solutions for: (i) automated identification of security vulnerabilities associated with data privacy and (ii) obfuscation of the identified vulnerabilities. For the identification and obfuscation of vulnerabilities, it is intended to apply artificial intelligence techniques. This project contributes to the scientific advancement of the Internet and to the improvement of technologies aimed at implementing the LGPD. The performance evaluations of the proposed solutions will be conducted in the cybersecurity test environment under development within the scope of the MCTIC/FAPESP MENTORED project. (AU)

Articles published in Agência FAPESP Newsletter about the research grant:
More itemsLess items
Articles published in other media outlets ( ):
More itemsLess items
VEICULO: TITULO (DATA)
VEICULO: TITULO (DATA)

Scientific publications
(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)
VERGUTZ, ANDRESSA; DOS SANTOS, BRUNA V.; KANTARCI, BURAK; NOGUEIRA, MICHELE. Data Instrumentation From IoT Network Traffic as Support for Security Management. IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT, v. 20, n. 2, p. 13-pg., . (18/23098-0, 21/06733-6)
BREZOLIN, UELINTON; VERGUTZ, ANDRESSA; NOGUEIRA, MICHELE. A method for vulnerability detection by IoT network traffic analytics. Ad Hoc Networks, v. 149, p. 10-pg., . (18/23098-0, 21/06733-6)

Please report errors in scientific publications list using this form.
X

Report errors in this page


Error details: