Enhancing Cyber Threat Detection with Bias-Resilient Machine Learning Models
By: Austin Johnson and Murthy Rallapalli
Abstract
Machine learning (ML) has transformed the cybersecurity landscape, enabling automated anomaly detection, intrusion prevention, and adaptive defense mechanisms. However, these models can exhibit inherent bias due to skewed training data, incomplete attack representations, and overfitting to specific threat profiles. This paper seeks to address a critical question: How can AI-driven threat detection systems be refined to minimize bias while maintaining high detection accuracy?
Bias in cybersecurity AI is comparable to biases found in other domains, such as loan approval models in banking, where historical data can reinforce unfair decision-making. Similarly, cybersecurity models trained on historical attack data may disproportionately label certain behaviors, regions, or users as high-risk, leading to false positives (flagging benign activities as threats) or false negatives (failing to detect actual attacks). For instance, security systems relying on behavioral analysis may over-prioritize past attack patterns, missing emerging threats or unfairly flagging atypical but non-malicious activity.
This research analyzes key bias-mitigation strategies, including adversarial training, balanced dataset curation, and explainable AI techniques, to develop a more inclusive and robust cybersecurity framework. By understanding and addressing these biases, organizations can improve threat detection reliability while reducing unintended security blind spots. Ensuring fairness in AI-driven security solutions is not just a computational challenge but a step toward more ethical and transparent cybersecurity practices.
Bias in cybersecurity AI is comparable to biases found in other domains, such as loan approval models in banking, where historical data can reinforce unfair decision-making. Similarly, cybersecurity models trained on historical attack data may disproportionately label certain behaviors, regions, or users as high-risk, leading to false positives (flagging benign activities as threats) or false negatives (failing to detect actual attacks). For instance, security systems relying on behavioral analysis may over-prioritize past attack patterns, missing emerging threats or unfairly flagging atypical but non-malicious activity.
This research analyzes key bias-mitigation strategies, including adversarial training, balanced dataset curation, and explainable AI techniques, to develop a more inclusive and robust cybersecurity framework. By understanding and addressing these biases, organizations can improve threat detection reliability while reducing unintended security blind spots. Ensuring fairness in AI-driven security solutions is not just a computational challenge but a step toward more ethical and transparent cybersecurity practices.
Language: English
Published on: Mar 20, 2025
Published by: University of North Georgia
In partnership with: Paradigm Publishing Services
© 2025 Austin Johnson, Murthy Rallapalli, published by University of North Georgia
This work is licensed under the Creative Commons Attribution 4.0 License.