Theses and Dissertations

Advisor

Gudla, Charan

Committee Member

Trawick, George

Committee Member

Chen, Jingdao

Date of Degree

5-16-2025

Original embargo terms

Visible MSU Only 1 year

Document Type

Graduate Thesis - Campus Access Only

Major

Computer Science (Research Computer Science)

Degree Name

Master of Science (M.S.)

College

James Worth Bagley College of Engineering

Department

Department of Computer Science and Engineering

Abstract

The gcore radar 2024 says, the number of DDoS attacks has been increased by 46% in 12 months. Supervised and unsupervised techniques struggle detecting DDoS attacks due to the scarcity of labeled attack samples and an overwhelming presence of benign traffic. In contrast PU- Learning offers a promising solutions by dividing the data into positive and unlabeled data. This study explores the effectiveness of PU-learning in detecting DDoS attacks by comparing it with unsupervised methods. This method employs PU Bagging, Two Step method and auto-encoder based models to extract meaningful patters from network traffic data, utilizing CICDDoS2017 dataset for evaluation. Proposed approach aims to improve generalizability and robustness of DDoS detection system, practically into real world scenarios where labeled data is scare. Experimental results demonstrate PU-learning can achieve competitive detection accuracy while reducing reliance on labeled negative samples. Our findings suggest PU-learning enhances adaptability to evolving attack patters, can be integrated into existing security infrastructure for more efficient DDoS mitigation

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