439 Final Project Presentation

CSCE 439 Project Proposal: Network Intrusion Detection with Machine Learning
Team Members:
Abel Cherian
Joshua Abraham
Dataset and Research Topic:
For this project, we will use the NSL-KDD dataset, which is widely recognized in cybersecurity research. This dataset contains network traffic data labeled as normal or intrusive, simulating real-world cyberattacks. Link: www.kaggle.com...
Research Question:
"Can modern machine learning classifiers improve the detection rate of cyber intrusions?"
Research Goals and Approach:
We aim to investigate the effectiveness of different machine learning algorithms in identifying network intrusions. Specifically, we will implement models such as Random Forest, Support Vector Machine (SVM), and Neural Networks to detect intrusions. To enhance performance, we will explore parameter tuning techniques and compare the results to findings from existing research.
This project will not only reproduce results from prior studies but will also introduce new insights by fine-tuning hyperparameters and testing various classifiers. Our focus will be to identify which algorithm and configuration yield the best detection accuracy.
Conclusion:
This project aligns with our interests in cybersecurity and will provide valuable hands-on experience in both data analysis and machine learning, preparing us for future roles in this field.