Title
Early Network Attack Identification
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
Recommended Citation
Kim, Minji, "Early Network Attack Identification" (2021). Electronic Theses & Dissertations. 353.
https://digitalcommons.tamuc.edu/etd/353
