Xavier Larriva Novo from RSTI research group, has disserted his Ph.D. thesis work in related topics to Cybersecurity and Artificial Intelligence
On January 13th our college Xavier Larriva Novo obtained the title of Ph.D. in Telematic System Engineering. The Ph.D. dissertation was titled “OPTIMIZATION OF INTRUSION DETECTION SYSTEMS BASED ON MACHINE LEARNING FOR CYBERSECURITY BY BOOSTING FEATURE ENGINEERING AND MODEL SELECTION TECHNIQUES” with the Professors Ph.D. Victor Villagra and Ph.D. Mario Vega Barbas as directors.
The PhD Thesis was developed in the context of intrusion detection systems based on Machine Learning techniques and contribute to the performance of intrusion detection systems based on feature engineering and Machine Learning model optimization.
The main objective of this PhD Thesis work was to design, implement and validate an intrusion detection system capable of reliably detecting cyber attacks. This system aims to respond to potential attacks by considering the optimization of different phases of a Machine Learning model, such as feature selection, data preprocessing, and model selection. This system is able to process information quickly and efficiently, considering the current state of the art in the area.
In this PhD thesis work, an intrusion detection system was designed based on feed-forward neural network and recurrent neural networks. Also, a characterization of cybersecurity datasets based on these models were developed. The result is a model that allows a characterization to be applied to different network scenarios for cybersecurity anomaly detection.
Based on this model, a distributed preprocessing model for intrusion detection systems based on Machine Learning was developed for real cybersecurity datasets with current attacks. The result was an intrusion detection system capable of processing real-world, large-scale cybersecurity datasets with high accuracy. In addition, this PhD Thesis proposed the development of a dynamic model selector capable of making the best prediction for individual Machine Learning-based intrusion detection systems, thus increasing the overall accuracy in terms of detection rate for different types of attacks. Finally, all contributions were evaluated against related state-of-the-art studies and are presented through this document as a compendium of articles for this PhD Thesis work.
Available on: https://doi.org/10.20868/UPM.thesis.69604
