L. F. Carvalho

发表

Digital Signature of Network Segment Using Genetic Algorithm and Ant Colony Optimization Metaheuristics

M. L. Proença, P. R. G. Hernandes, L. F. Carvalho, 2014 .

A GRU deep learning system against attacks in software defined networks

Jaime Lloret, Marcos V. O. de Assis, Luiz Fernando Carvalho, 2021, J. Netw. Comput. Appl..

A comprehensive survey on network anomaly detection

J. Al-Muhtadi, M. L. Proença, L. F. Carvalho, 2018, Telecommunication Systems.

Fast Defense System Against Attacks in Software Defined Networks

Matheus P. Novaes, Marcos V. O. De Assis, Cinara B. Zerbini, 2018, IEEE Access.

Unsupervised learning clustering and self-organized agents applied to help network management

Sylvio Barbon Junior, Luiz Fernando Carvalho, Mario Lemes Proença, 2016, Expert Syst. Appl..

Digital signature to help network management using flow analysis

Joel J. P. C. Rodrigues, Marcos V. O. de Assis, Luiz Fernando Carvalho, 2016, Int. J. Netw. Manag..

Network Anomaly Detection System using Genetic Algorithm and Fuzzy Logic

Taufik Abrão, Luiz Fernando Carvalho, Mario Lemes Proença, 2018, Expert Syst. Appl..

A comprehensive survey on network anomaly detection

Joel J. P. C. Rodrigues, Luiz Fernando Carvalho, Mario Lemes Proença, 2018, Telecommunication Systems.

Near real-time security system applied to SDN environments in IoT networks using convolutional neural network

Joel J. P. C. Rodrigues, Jaime Lloret, Marcos V. O. de Assis, 2020, Comput. Electr. Eng..

Adversarial Deep Learning approach detection and defense against DDoS attacks in SDN environments

Luiz Fernando Carvalho, Mario Lemes Proença, Matheus P. Novaes, 2021, Future Gener. Comput. Syst..

Long Short-Term Memory and Fuzzy Logic for Anomaly Detection and Mitigation in Software-Defined Network Environment

Jaime Lloret, Matheus P. Novaes, Luiz F. Carvalho, 2020, IEEE Access.

Artificial Immune Systems and Fuzzy Logic to Detect Flooding Attacks in Software-Defined Networks

Sylvio Barbon Junior, Sylvio Barbon, Luiz F. Carvalho, 2020, IEEE Access.

An ecosystem for anomaly detection and mitigation in software-defined networking

Taufik Abrão, Luiz Fernando Carvalho, Mario Lemes Proença, 2018, Expert Syst. Appl..

Unsupervised online anomaly detection in Software Defined Network environments

Sylvio Barbon Junior, M. L. Proença, L. F. Carvalho, 2021, Expert Syst. Appl..

Statistical, forecasting and metaheuristic techniques for network anomaly detection

Joel J. P. C. Rodrigues, Luiz Fernando Carvalho, Mario Lemes Proença, 2015, SAC.

A novel anomaly detection system to assist network management in SDN environment

Joel J. P. C. Rodrigues, Luiz Fernando Carvalho, Mario Lemes Proença, 2017, 2017 IEEE International Conference on Communications (ICC).

Network anomaly detection using IP flows with Principal Component Analysis and Ant Colony Optimization

Joel J. P. C. Rodrigues, Luiz Fernando Carvalho, Mario Lemes Proença, 2016, J. Netw. Comput. Appl..

A Gated Recurrent Unit Deep Learning Model to Detect and Mitigate Distributed Denial of Service and Portscan Attacks

Matheus P. Novaes, Jaime Lloret, M. L. Proença, 2022, IEEE Access.

ACO and GA metaheuristics for anomaly detection

Luiz Fernando Carvalho, Mario Lemes Proença, Anderson H. Hamamoto, 2015, 2015 34th International Conference of the Chilean Computer Science Society (SCCC).

Digital Signature of Network Segment using Flow Analysis

Taufik Abrão, Luiz Fernando Carvalho, Mario Lemes Proença, 2016, DCNET/ICE-B/OPTICS.