## 3 Methodology
### Rlfoula Framework Description
#### 3.2.3 Employing Functional Agents in RL
In Proceedings of the 14 th International Conference on Modelling and Simulation, Cambridge, UK, 28-30 March 2012; pp. 145-150. * [7] [PERSON]; [PERSON] Intrusion detection using random forests classifier with SMOTE and feature reduction. In Proceedings of the International Conference on Cloud & Ubiquitous Computing & Emerging Technologies, Pune, India, 15-16 November 2013; pp. 127-132. * [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON] Variational data generative model for intrusion detection. _Knowl. Inf. Syst._**2019**, _60_, 569-590. [CrossRef]
* [PERSON] and [PERSON] (2016) [PERSON]; [PERSON] SMOTE-NCL: A re-sampling method with filter for network intrusion detection. In Proceedings of the 2 nd IEEE International Conference on Computer and Communications, Sanya, China, 27-29 December 2016; pp. 1157-1161. * [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON] Increasing the Performance of Machine Learning-Based IDSs on an Imbalanced and Up-to-Date Dataset. _IEEE Access_**2020**, \(8\), 32150-32162. [CrossRef]
* [PERSON] and [PERSON] (2018) [PERSON]; [PERSON] LA-GRU: Building combined intrusion detection model based on imbalanced learning and gated recurrent unit neural network. _Secur. Commun. Netw._**2018**, _2018_, 1-13. [CrossRef]
* [PERSON] and [PERSON] (2012) [PERSON]; [PERSON]; [PERSON] Intrusion Detection System Using Machine Learning for Vehicular Ad Hoc Networks Based on ToN-IoT Dataset. _IEEE Access_**2012**, \(9\), 142206-142217. [CrossRef]
* [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON] Enhanced Decision Tree--J48 With SMOTE Machine Learning Algorithm for Effective Botnet Detection in Imbalanced Dataset. In Proceedings of the15 th International Conference on Electronics Computer and Computation, Abuja, Nigeria, 10-12 December 2019; pp. 1-6. * [PERSON] and [PERSON] (2018) [PERSON]; [PERSON] _Reinforcement Learning: An Introduction_; The MIT Press: Cambridge, MA, USA, 2018. * [PERSON] and [PERSON] (2009) [PERSON]; [PERSON] Multi-Agent Reinforcement Learning for Intrusion Detection. Ph.D. Thesis, University of York, York, UK, 2009. * [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Towards experienced anomaly detector through reinforcement learning. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, LA, USA, 2-7 February 2018; pp. 1-6. * [PERSON] et al. (2015) [PERSON]; [PERSON]; [PERSON] Generic and scalable framework for automated time-series anomaly detection. In Proceedings of the 21 th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'15, Sydney, Australia, 10-13 August 2015; pp. 1939-1947. * [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON] Adversarial environment reinforcement learning algorithm for intrusion detection. _Comput. Netw._**2019**, _159_, 96-109. [CrossRef]
* [PERSON] and [PERSON] (2021) [PERSON]; [PERSON] [PERSON] Intrusion Detection Systems: Conceptual Study and Review. In Proceedings of the 2021 6 th International Conference on Signal Processing, Computing and Control, Xi'an, China, 9-11 April 2021; pp. 694-700. * [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]. Benchmarking of Machine Learning for Anomaly Based Intrusion Detection Systems in the CICIDS2017 Dataset. _IEEE_**2021**, \(9\), 1-6. [CrossRef]
* A Realistic Cyber Defense Dataset (CSE-CIC-IDS2018). Available online: [[https://registry.opendata.aws/cse-cicids2018](https://registry.opendata.aws/cse-cicids2018)]([https://registry.opendata.aws/cse-cicids2018](https://registry.opendata.aws/cse-cicids2018)) (accessed on 1 January 2022). * [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON] [PERSON] An Intrusion Detection System for Imbalanced Dataset Based on Deep Learning. In Proceedings of the 2021 9 th International Symposium on Computing and Networking, Matsue, Japan, 23-26 November 2021; pp. 38-47. * [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Balancing Approaches towards ML for IDS: A Survey for the CSE-CIC IDS Dataset.