Title

Early Network Attack Identification

Author

Minji Kim

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science and Info Sys

Date of Award

Spring 2021

Abstract

Early attack identification is critical to secure the network exposed to various cyber-attack risks in modern times. Anomaly detection is also an important task, but devising a relevant strategy against the malicious event may be limited due to lacking attack information. For early attack identification, the main challenges are (1) imbalanced class data, (2) limited availability network datasets for early identification, and (3) determining variables to improve performance. To deal with these challenges, this research will explore early-identification of attacks with the following two angles: (1) Deep learning models including ANN, RNN; (2) Data augmentation for balanced data. Based on the analysis of the result of the experiments, this study will develop a deep learning model for early attack identification for validation purposes.

Advisor

Jinoh Kim

Subject Categories

Computer Sciences | Physical Sciences and Mathematics

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