To detect and mitigate bias in cybersecurity datasets (for intrusion detection or phishing classification), systematically analyze for data imbalances using statistical tools and domain-discrepancy algorithms, such as distribution comparison or domain discrimination. Employ mitigation techniques like oversampling minority classes, data reweighting, and fairness-aware learning, while continually updating datasets to reflect emerging threats and reduce sampling or feature bias. Ensuring diverse, representative, and regularly audited data—paired with transparent, explainable models—effectively reduces false positives and enhances real-world robustness.

Hello, of course, it depends on the type of data, task, and what do you mean by "bias". General recommendations for cybersecurity research were discussed in "Dos and Don'ts of Machine Learning in Computer Security" by D. Arp. et al at USENIX 2022. Really a must-read paper for anyone working in cybersec using ML.

If you are more interested in intrusion detection, particularly Network Intrusion detection, I'd gladly invite you to take a look on our paper "Network Intrusion Datasets: A Survey, Limitations, and Recommendations" which we recently published in Computers & Security, with a preprint available through my profile.

Cheers.

Practitioners perform exploratory data analysis (EDA) to examine feature distributions, class imbalances, and source diversity.

To detect systematic bias, use: Statistical parity checks, confusion matrix analysis across subgroups, and outlier detection.

Mitigation measures:

-Data augmentation could be used to balance classes.

-Re-sampling techniques for oversampling minority classes and undersampling dominant classes.

-Fairness-aware algorithms that optimize for accuracy and equity.

To reduce bias and ensure robust, fair, and generalized threat detection systems:

-Incorporate diverse data sources

-Continuously validate models against real-world traffic

-Establish human-in-the-loop review processes