Publication Date
Spring 2025
Degree Type
Master's Project
Degree Name
Master of Science in Computer Science (MSCS)
Department
Computer Science
First Advisor
Navrati Saxena
Second Advisor
Amith Kamath Belman
Third Advisor
Pooja Shyamsundar
Keywords
Out-of-Distribution Detection, Encrypted Traffic Classification, Uncertainty Quantification, Machine Learning, Dimensionality Reduction, Neural Networks, Expected Calibration Error, Cybersecurity
Abstract
The changes occurring in the amount of encrypted network traffic is growing at an alarming rate. This development has created intricate problems in traffic classification which is vital for effective cybersecurity. Moreover, most frameworks seem to ignore OOD detection, model calibration and novel pattern detection as cornerstone problem areas. The due analysis is presented as a machine learning approach aimed at resolving encrypted traffic classification issues and focuses on novel OOD detection and calibration issues. Primary contributions comprise detection of out-of-distribution states using softmax scaled cosine similarity, advanced variance-based feature elimination, and lowering ECE using stringent NNs. This work demonstrates that ensemble/models using a blend of these techniques using a dataset of VPN encrypted and unencrypted network traffic achieve reliable and high-accurate performance across different classes of traffic. The results underscore the impact of the proposed solutions on improving dynamics, reliability, and interpretability of machine learning models and through them secure and intelligent monitoring of network traffic.
Recommended Citation
Edupuganti, Prabhat, "Machine Learning based Network Traffic Classification with Cosine-Similarity based Out-Of-Distribution Detection" (2025). Master's Projects. 1473.
DOI: https://doi.org/10.31979/etd.m4tt-7dvb
https://scholarworks.sjsu.edu/etd_projects/1473
