Full metadata
Title
A Network-Based Intrusion Prevention Approach for Cloud Systems Using XGBoost and LSTM Models
Description
The advancement of cloud technology has impacted society positively in a number of ways, but it has also led to an increase in threats that target private information available on cloud systems. Intrusion prevention systems play a crucial role in protecting cloud systems from such threats. In this thesis, an intrusion prevention approach todetect and prevent such threats in real-time is proposed. This approach is designed for network-based intrusion prevention systems and leverages the power of supervised machine learning with Extreme Gradient Boosting (XGBoost) and Long Short-Term Memory (LSTM) algorithms, to analyze the flow of each packet that is sent to a cloud system through the network. The innovations of this thesis include developing a custom LSTM architecture, using this architecture to train a LSTM model to identify attacks and using TCP reset functionality to prevent attacks for cloud systems. The aim of this thesis is to provide a framework for an Intrusion Prevention System. Based on simulations and experimental results with the NF-UQ-NIDS-v2 dataset, the proposed system is accurate, fast, scalable and has a low rate of false positives, making it suitable for real world applications.
Date Created
2023
Contributors
- Gianchandani, Siddharth (Author)
- Yau, Stephen (Thesis advisor)
- Zhao, Ming (Committee member)
- Lee, Kookjin (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
68 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.2.N.190927
Level of coding
minimal
Cataloging Standards
Note
Partial requirement for: M.S., Arizona State University, 2023
Field of study: Computer Science
System Created
- 2023-12-14 01:52:09
System Modified
- 2023-12-14 01:52:15
- 10 months 3 weeks ago
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