Cybersecurity Anomaly Detection in Adversarial Environments
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Authors
Bierbrauer, David A.
Kritzer, Will
Chang, Alexander
Bastian, Nathaniel D.
Issue Date
2021
Type
Conference presentations, papers, posters
Language
Keywords
Internet of Battlefield Things (IoBT)
Alternative Title
Abstract
The proliferation of interconnected battlefield information sharing devices, known as the Internet of Battlefield Things (IoBT), introduced several security challenges. Inherent to the IoBT operating environment is the practice of adversarial machine learning, which attempts to circumvent machine learning models. This work examines the feasibility of cost-effective unsupervised learning and graph-based methods for anomaly detection in the network intrusion detection system setting, and also leverages an ensemble approach to supervised learning of the anomaly detection problem. We incorporate a realistic adversarial training mechanism when training supervised models to enable strong classification performance in adversarial environments. The results indicate that the unsupervised and graph-based methods were outperformed in detecting anomalies (malicious activity) by the supervised stacking ensemble method with two levels. This model consists of three different classifiers in the first level, followed by either a Naive Bayes or Decision Tree classifier for the second level. The model maintains an F1-score above 0.97 for malicious samples across all tested level two classifiers. Notably, Naive Bayes is the fastest level two clas sifier averaging 1.12 seconds while Decision Tree maintains the highest AUC score of 0.98.
Description
Citation
Bierbrauer, David, Alexander R. Chang, Will Kritzer, and Nathaniel D. Bastian. 2021. “Cybersecurity Anomaly Detection in Adversarial Environments.” arXiv (Cornell University), May. https://doi.org/10.48550/arxiv.2105.06742.
Publisher
Cornell University