Date of Award


Document Type


Degree Name

Master of Science

Degree Discipline

Electrical Engineering


In this era of web technology driven by social networks, cloud computing, big data, and E-business, technology is also rapidly evolving. Most of the information is stored and managed via the Internet. With an increase in these development tools and techniques, cyber-crime is constantly increasing. The level of damage these attacks cause to the system affects the organizations to the core. Contemporary Deep Learning and Machine Learning technologies have become the popular choice of intrusion detection systems for the detection and prediction of cyber-attack. Similarly, cyber-security visualization is also an integral and essential part of monitoring network traffic and optimization. Abundant work has already been done to detect attacks, but monitoring these attacks still appears as elusive as detection for cyber analysts. However, the current open-source visualization tool has not been integrated with Deep Learning models to gain intelligence on the network. While many researchers [3] are already working on cyber-attack defense mechanisms, this research also takes advantage of Deep Learning and Machine Learning technologies to contribute to the work against such crimes. A novel Deep Learning enhanced visualization tool is also proposed for malicious traffic node prediction and monitoring. The proposed method exploits the intriguing properties of Deep Learning models to gain intelligence for network monitoring. A real-world DARPA dataset has been used to validate the proposed method.

Index Terms—Cyber-security, data analysis, data science, darpa-dataset, decision tree, deep learning, deep neural network, DL model, ML model, network analysis tool, network monitoring tool, supervised learning, support vector machine, visualization tool.

Committee Chair/Advisor

Lijun Qian

Committee Member

Xiangfang Li

Committee Member

Xishuang Dong

Committee Member

Pamela Obiomon


Prairie View A&M University


© 2021 Prairie View A & M University

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Date of Digitization


Contributing Institution

John B Coleman Library

City of Publication

Prairie View





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