Abstract
Nowadays, Machine learning models are widely used in many fields and employed to solve problems from different sectors. However, we often face issues when running these models in case the training data is insufficient. These issues happen when the dataset available is small or only part of it is available because of its sensitive content or even because the dataset is imbalanced. Thus, synthetic data generation is needed to provide data similar to actual data. We have proposed the use of the Logical Analysis of Data methodology to generate adversarial data from any given dataset. For our study, we have used an intrusion detection dataset, and the results demonstrate the potential of Logical Analysis of Data by evaluating adversarial datasets using various machine learning classifiers.
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Chauhan, S., Mahmoud, L., Sheth, T., Gangopadhyay, S., Gangopadhyay, A.K. (2023). Generating Adversarial Examples Using LAD. In: Quaresma, P., Camacho, D., Yin, H., Gonçalves, T., Julian, V., Tallón-Ballesteros, A.J. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2023. IDEAL 2023. Lecture Notes in Computer Science, vol 14404. Springer, Cham. https://doi.org/10.1007/978-3-031-48232-8_15
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