Ángel Luis Perales Gómez

Orcid: 0000-0003-1004-881X

According to our database1, Ángel Luis Perales Gómez authored at least 15 papers between 2018 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2023
VAASI: Crafting valid and abnormal adversarial samples for anomaly detection systems in industrial scenarios.
J. Inf. Secur. Appl., December, 2023

Behavioral fingerprinting to detect ransomware in resource-constrained devices.
Comput. Secur., December, 2023

An interpretable semi-supervised system for detecting cyberattacks using anomaly detection in industrial scenarios.
IET Inf. Secur., July, 2023

SUSAN: A Deep Learning based anomaly detection framework for sustainable industry.
Sustain. Comput. Informatics Syst., January, 2023

TemporalFED: Detecting Cyberattacks in Industrial Time-Series Data Using Decentralized Federated Learning.
CoRR, 2023

Fedstellar: A Platform for Decentralized Federated Learning.
CoRR, 2023

2022
FARMIT: continuous assessment of crop quality using machine learning and deep learning techniques for IoT-based smart farming.
Clust. Comput., 2022

A Methodology for Evaluating the Robustness of Anomaly Detectors to Adversarial Attacks in Industrial Scenarios.
IEEE Access, 2022

2021
SafeMan: A unified framework to manage cybersecurity and safety in manufacturing industry.
Softw. Pract. Exp., 2021

Crafting Adversarial Samples for Anomaly Detectors in Industrial Control Systems.
Proceedings of the 12th International Conference on Ambient Systems, 2021

2020

MADICS: A Methodology for Anomaly Detection in Industrial Control Systems.
Symmetry, 2020

2019
Intelligent and Dynamic Ransomware Spread Detection and Mitigation in Integrated Clinical Environments.
Sensors, 2019

On the Generation of Anomaly Detection Datasets in Industrial Control Systems.
IEEE Access, 2019

2018
A Self-Adaptive Deep Learning-Based System for Anomaly Detection in 5G Networks.
IEEE Access, 2018


  Loading...