An effective anomaly-based intelligent IDS (AN-Intel-IDS) must detect both known and unknown attacks. Hence, there is a need to train AN-Intel-IDS using dynamically generated, real-time data in an adversarial setting. Unfortunately, the public datasets available to train AN-Intel-IDS are ineluctably static, unrealistic, and prone to obsolescence. Further, the need to protect private data and conceal sensitive data features has limited data sharing, thus encouraging the use of synthetic data for training predictive and intrusion detection models. However, synthetic data can be unrealistic and potentially bias. On the other hand, real-time data are realistic and current; however, it is inherently imbalanced due to the uneven distribution of anomalous and non-anomalous examples. In general, non-anomalous or normal examples are more frequent than anomalous or attack examples, thus leading to skewed distribution. While imbalanced data are commonly predominant in intrusion detection applications, it can lead to inaccurate predictions and degraded performance. Furthermore, the lack of real-time data produces potentially biased models that are less effective in predicting unknown attacks. Therefore, training AN-Intel-IDS using imbalanced and adversarial learning is instrumental to their efficacy and high performance. This paper investigates imbalanced learning and adversarial learning for training AN-Intel-IDS using a qualitative study. It surveys and synthesizes generative-based data augmentation techniques for addressing the uneven data distribution and generative-based adversarial techniques for generating synthetic yet realistic data in an adversarial setting using rapid review, structured reporting, and subgroup analysis.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.