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RandomForestNN Classification for Adversarial AI Black-Box Techniques on MITRE ATT&CK Labeled Data
Journal article   Open access   Peer reviewed

RandomForestNN Classification for Adversarial AI Black-Box Techniques on MITRE ATT&CK Labeled Data

Mink Dustin, Anthony Simpson, Sikha Bagui and Subhash Bagui
Electronics (Basel), Vol.15(12), p.2598
06/15/2026
DOI: https://doi.org/10.3390/electronics15122598
Web of Science ID: WOS:001803684500001

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Abstract

black-box optimization adversarial AI MITRE ATT&CK network security HopSkipJump attack Simultaneous Perturbation Stochastic Approximation Attack Square Attack Adversarial Robustness Toolbox Computer Science Cybersecurity Electronic Neural Network
Research examining the security of network intrusion detection systems is vital for protecting modern digital infrastructure from increasingly sophisticated threats. This study investigates how machine learning network security models, trained with tactical frameworks like MITRE ATT&CK, respond to adversarial examples crafted through black-box optimization techniques. Using three attack algorithms, HopSkipJump, Simultaneous Perturbation Stochastic Approximation Attack and the Square Attack algorithms, we demonstrate that the Random Forest model remains vulnerable despite tactical framework integration. For example, the HopSkipJump attack achieved a 92% success rate in causing malicious traffic to appear benign. Our analysis reveals which network traffic features are most susceptible to manipulation, with model performance metrics declining significantly under adversarial conditions. These findings highlight an important gap between theoretical security frameworks and practical implementation that must be addressed to develop more robust defense systems. By identifying specific vulnerabilities, this research contributes valuable insights that can inform improved adversarial robustness in operational network security environments.
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Published (Version of record) link to article Open CC BY V4.0

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