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Book chapter
Passive Operating System Fingerprinting Analysis Using Artificial Intelligence Techniques
Proceedings of the 2023 International Conference on Advances in Computing Research (ACR’23), pp.178-191
Lecture Notes in Networks and Systems, Springer Nature Switzerland
05/27/2023
Abstract
Modern enterprise networks are complex and present countless security challenges. Understanding the nature of the systems that exist within a network environment is a vital step in securing such environments. Therefore, operating systems on the network must be identified, tracked, and continuously monitored. In this research, we consider the problem of detecting unauthorized operating systems on an enterprise network, which could exist because of the unintentional actions of an authorized user or the unauthorized actions of internal users or external attackers. We intend to utilize an artificial neural network-based classifier [ANN], which will be developed using the PyTorch and fastai deep learning libraries. Simulated network traffic has been generated through the implementation of two separate virtual network environments, and the generated traffic was passively collected and analyzed prior to traversing the network boundary. The performance evaluation of the neural network classifier will be analyzed using the collected data in this research.
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Details
- Title
- Passive Operating System Fingerprinting Analysis Using Artificial Intelligence Techniques
- Publication Details
- Proceedings of the 2023 International Conference on Advances in Computing Research (ACR’23), pp.178-191
- Resource Type
- Book chapter
- Publisher
- Springer Nature Switzerland; Cham
- Series
- Lecture Notes in Networks and Systems
- Number of pages
- 14
- Copyright
- © 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
- Identifiers
- 99381488937106600
- Academic Unit
- Cybersecurity and Information Technology; Hal Marcus College of Science and Engineering
- Language
- English