Shelby Hall Graduate Research Forum Posters
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Description
With the rise of cyber threats, cybersecurity continues to play a critical role in the ever-changing landscape of technology by protecting and defending against threat agents. Our research applies novel machine learning (ML)techniques to detect network intrusions effectively. Our primary focus is to extend prior research, which has used network flows that are processed by a nonlinear phase space algorithm (NLPSA). The NLSPA approach has proven extremely effective in detecting anomalous or malicious traffic patterns on representative data but requires extensive training time.
Our contribution integrates deep learning into the anomaly detection approach by creating image-based representations of the adjacency matrices taken from the phase space graphs produced by NLPSA. These images are used as input for training a Convolutional Neural Network (CNN) as part of an anomaly-based network intrusion detection system (NIDS). To help validate and extend prior NLPSA work, we use the same well-known NIDS dataset (CIC-IDS2018) and similar experimental configurations to understand how deep learning might improve the efficiency and accuracy of the NLPSA approach. Overall, we achieved a ~90-95% detection accuracy using three CNN models with NLPSA for NID.
Publication Date
3-2026
Department
Computer Science
Disciplines
OS and Networks | Other Computer Sciences | Systems Architecture
Recommended Citation
Suon, Chakriya, "Integrating Nonlinear Phase Space Analysis and Image-Based Representation for Network Intrusion Detection" (2026). Shelby Hall Graduate Research Forum Posters. 41.
https://jagworks.southalabama.edu/southalabama-shgrf-posters/41