Shinwoo
Kim

GRASP-HPO

Hyperparameter Optimization using GRASP-Based Techniques

Python Ray Scikit-learn Machine Learning

This project aims to adapt GRASP (Greedy Randomized Adaptive Search Procedure) in optimizing hyperparameter that are used to train machine learning models in the context of Intrusion Detection Systems (IDS).

Background

This project is built on top of existing work1 by S. E. Quincozes, et al. which used a GRASP-based technique for feature selection in electric substation’s instrusion detection systems which utilize machine learning algorithms.

Practical Application of Machine Learning in IDSs
Practical Application of Machine Learning in IDSs

This project was developed a part of the CS 1980 Capstone course at the University of Pittsburgh. The main contributors are: Shinwoo Kim, Enoch Li, Jack Bellamy, Zi Han Ding, Zane Kissel, Gabriel Otsuka.

This project was sponsored and directed by Dr. Daniel Mossé2 and Dr. Silvio E. Quincozes3.

GitHub

  1. S. E. Quincozes, D. Mossé, D. Passos, C. Albuquerque, L. S. Ochi and V. F. dos Santos, “On the Performance of GRASP-Based Feature Selection for CPS Intrusion Detection,” in IEEE Transactions on Network and Service Management, vol. 19, no. 1, pp. 614-626, March 2022, doi: 10.1109/TNSM.2021.3088763. ↩

  2. Professor of Computer Science, University of Pittsburgh, USA ↩

  3. Professor, Federal University of Pampa [UNIPAMPA], Brazil ↩