Adversarial Attack Optimization and Evaluation for Machine Learning-Based Dark Web Traffic Analysis
Document Type
Conference Proceeding
Publication Date
10-19-2024
School
Computing Sciences and Computer Engineering
Abstract
Machine learning (ML) is quickly becoming one of the most transformative technologies in the field of computing. Applications of ML are wide-spread and growing exponentially, revolutionizing the future of major industries such as finance, healthcare, automotives, and more. This has made it more necessary than ever to recognize the instability created by adversarial attacks—the deliberate manipulation of data to mislead ML models. This instability must be addressed through researching the effects of adversarial attacks and how they can be better recognized. Our research explored the use of adversarial attacks in dark web network traffic analysis by first improving our understanding of how adversarial attacks could be optimized. We manipulated a dataset of dark web traffic data through the analysis of confusion matrices and Euclidean distances, aiming to cause maximum confusion for each of our models. We then trained and tested each model in a variety of scenarios to further our understanding of weaknesses in both the traffic data and the machine learning techniques employed.
Publication Title
Communications in Computer and Information Science
Volume
2244 CCIS
First Page
3
Last Page
13
Recommended Citation
Harrison, N.,
Broome, H.,
Shrestha, Y.,
Robles, A.,
Gautam, A.,
Rahimi, N.
(2024). Adversarial Attack Optimization and Evaluation for Machine Learning-Based Dark Web Traffic Analysis. Communications in Computer and Information Science, 2244 CCIS, 3-13.
Available at: https://aquila.usm.edu/fac_pubs/21841
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