Identifying and obfuscating security vulnerabilities and behaviors in IoT

• Grant number: 21/06733-6
• Support Opportunities: Regular Research Grants
• Start date: July 01, 2022
• End date: June 30, 2024
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computer Systems
• Agreement: MCTI/MC
• Principal Investigator: Aldri Luiz dos Santos
• Grantee: Aldri Luiz dos Santos
• Host Institution: Instituto de Ciências Exatas (ICEx). Universidade Federal de Minas Gerais (UFMG). Ministério da Educação (Brasil). Belo Horizonte , SP, Brazil
• Associated researchers: Aurora Trinidad Ramirez Pozo ; Michele Nogueira Lima ; Ricardo Tombesi Macedo
• Associated scholarship(s): 22/16444-4 - An experimental performance evaluation for identifying and obfuscating security vulnerabilities and IoT behavior, BP.TT; 22/15573-5 - A mechanism for obfuscating behaviors in IoT based on adversarial machine learning, BP.TT; 22/14299-7 - Modeling and implementation of data classification for automated vulnerability identification in IoT devices, BP.TT

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)

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., 2023-06-01. (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., 2023-10-01. (18/23098-0, 21/06733-6)