Towards Intelligent Security Mechanisms for Connected Things-Reference-Cited by-同舟云学术

Towards Intelligent Security Mechanisms for Connected Things

Published:2024-07-21 Issue: Volume: Page:11-20
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Container-title:Anais do XXXVII Concurso de Teses e Dissertações (CTD 2024)
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Author:

Araujo-Filho Paulo Freitas de,Campelo Divanilson R.,Kaddoum Georges

Abstract

The widespread adoption of connected devices and the adoption of machine learning enable attackers to launch several cyber-attacks and adversarial attacks. Therefore, the goals of this thesis are to investigate and develop cutting-edge solutions to enhance the security of systems by effectively and efficiently detecting cyber-attacks while also defending systems that rely on ML from adversarial attacks. The main results of our thesis comprehend multiple awards, the publication of eight papers in prestigious journals, three conference papers, two patents, and one software registration. Furthermore, our research has been recognized and awarded as one of the two 2022 Microsoft Research Ph.D. Fellowship recipients in Security, Privacy, and Cryptography worldwide.

Publisher

Sociedade Brasileira de Computação - SBC

Reference13 articles.

1. Freitas de Araujo-Filho, P., Kaddoum, G., Campelo, D. R., Gondim Santos, A., Macêdo, D., and Zanchettin, C. (2021). Intrusion Detection for Cyber–Physical Systems Using Generative Adversarial Networks in Fog Environment. IEEE Internet of Things J., 8(8):6247–6256.

2. Li, D., Chen, D., Jin, B., Shi, L., Goh, J., and Ng, S.-K. (2019). MAD-GAN: Multivariate anomaly detection for time series data with generative adversarial networks. In Springer Int. Conf. on Artif. Neural Netw., pages 703–716.

3. Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., and Frossard, P. (2017). Universal Adversarial Perturbations. In Proc. of the IEEE Conf. on Comput. Vision and Pattern Recognit. (CVPR).

4. Nisioti, A., Mylonas, A., Yoo, P. D., and Katos, V. (2018). From Intrusion Detection to Attacker Attribution: A Comprehensive Survey of Unsupervised Methods. IEEE Commun. Surveys & Tut., 20(4):3369–3388.

5. O’Shea, T. J. and West, N. (2016). Radio Machine Learning Dataset Generation with GNU Radio. Proc. of the 6th GNU Radio Conf.