# An Intrusion Detection System Using BoT-IoT
## 2 Related Works
Through the use of the net flow meter feature set, the method is assessed using the NF-BoT-IoT-v2, NF-ToN-IoT-v2, and IoTDS20 datasets [16]. [PERSON] et al. propose a distributed IDS using fog computing to detect DDoS attacks against mining pools in blockchain-enabled IoT networks. The proposed model is evaluated using Random Forests and an optimized gradient tree boosting system on distributed fog nodes and is tested on the BoT-IoT dataset. Results show that XGBoost outperforms binaryattack detection, while the Random Forest outperforms multi-attack detection. Additionally, the Random Forest takes less time for training and testing on distributed fog nodes than XGBoost [17]. [PERSON] et al. present a deep-learning-based intrusion detection system for IoT DDoS botnet attacks using a dataset created in a realistic network environment. A highly extensible Deep Neural Network (DNN) is developed and evaluated for headstrong detection of IoT botnet attacks. The results show that the proposed DNN outperforms existing systems with high accuracy and precision, demonstrating its potential for effectively detecting IoT DDoS botnet attacks [18]. [PERSON] et al. present an improved PIO feature selection model for intrusion detection. The algorithm uses ensemble learning for detection. The dataset and architecture used in these modes is not complex [19]. [PERSON] et al. discuss an RNN-based model for an intrusion detection system in IoT networks. In comparison to models trained on the full feature set, the models generated on the smaller dataset had higher recall rates without losing the capacity to distinguish between classes [20]. In the past, few researchers applied deep learning algorithms for intrusion detection systems. Many intrusion detection systems currently in use do not take into account the issue of dataset imbalance or model maintenance [21; 22]. This can result in high levels of bias, as well as high false-positive and false-negative rates, which in turn can lead to security breaches. It is important to address these issues in order to improve the accuracy and effectiveness of intrusion detection systems, and ultimately reduce the risk of security breaches [23; 24; 25]. [PERSON] et al. developed an intrusion detection model specifically for IoT environments, utilizing a K-Nearest Neighbors (K-NN) classifier and feature selection techniques. This model involves constructing a NIDS (Network Intrusion Detection System) based on the K-NN algorithm, with the aim of improving the accuracy. PCA is used for the feature selection [26]. [PERSON] et al. discuss an approach to addressing the issue of class imbalance in machine learning by utilizing ensemble learning techniques. Specifically, they employed the Bagging classifier, which utilizes a Deep Neural Network (DNN) as its base estimator. In the proposed approach, class weights were introduced during the training process of the DNN, with the aims of creating balanced training subsets for the model [27]. This allows for improving the accuracy and reliability of the model, by ensuring that it is trained on a more representative and balanced dataset, and can better handle the complexities associated with class imbalance in machine learning [28]. Machine learning (ML) and deep learning (DL) algorithms can significantly enhance the effectiveness of intrusion detection systems (IDSs) as a security tool [29]. [PERSON] et propose an improved IDS that utilizes gradient boosting (GB) and decision tree (DT) algorithms, implemented through the open-source Catboost, for IoT Security. The approach aims to improve the accuracy and reliability of the IDS by incorporating these advanced machine learning techniques, which are designed to better handle the complexities and challenges associated with IoT security [30]. The performance of IDS can enhance and improve the detection of potential threats and vulnerabilities in IoT environments [31]. Table 1 presents a summary of related work.