# Evaluating Ensemble Learning Mechanisms for Predicting Advanced Cyber Attacks
## 2 Related Work  
The use of machine learning techniques in NIDS has gained significant attention in the research community [13,14]. NIDS plays a crucial role in monitoring network traffic to identify potential cyberattacks. Machine learning algorithms help in identifying patterns and behaviors in network traffic that may indicate malicious activities. These algorithms learn from historical data and are capable of detecting new and previously unseen attacks. One approach involves developing predictive models based on historical data to identify specific attack behaviors. By categorizing network traffic into different classes such as legitimate, suspicious, and malicious, machine learning algorithms assist in reducing false positives and improving the accuracy of NIDS. Numerous works have focused on using machine learning techniques, including ensemble methods, for cyberattack prediction [15,16]. Supervised machine learning algorithms have been widely employed to classify network traffic into different classes. By training rule-based models on diverse datasets, NIDS can achieve more accurate classification and reduce false positives. On the other hand, unsupervised machine learning techniques have been utilized to analyze clustering and association patterns in network data, which can assist in identifying unknown attack patterns. However, in this paper, we primarily focus on supervised machine learning approaches based on binary classification. In the systematic review [17], authors discussed different ensemble techniques, including bagging, boosting, and stacking. Bagging involves training multiple classifiers on different subsets of the dataset and aggregating their predictions. Boosting, on the other hand, focuses on sequentially training weak learners, with each subsequent learner learning from the mistakes of the previous ones. Stacking is a two-level ensemble approach that combines the predictions of multiple base classifiers using another meta-classifier. The review also emphasized the advantages of ensemble learning in NIDS. By combining multiple classifiers, ensemble methods can reduce the number of false positives and enhance the overall detection accuracy. They can effectively handle imbalanced datasets, improve robustness against adversarial attacks, and handle concept drift, where the underlying data distribution changes over time. The review paper [18] presents a comprehensive review of ensemble learning techniques for intrusion detection. They explored various ensemble strategies and their applications in the context of NIDS. The study highlighted that ensemble methods can effectively handle complex and diverse data, improving the detection of both known and unknown attacks. Authors in [19] present an adaptive ensemble machine learning model for NIDS. The combination of individual models is dynamically modified, and the model weight is also adjusted in a flexible way. Multiple decision trees, random forest, kNN, and DNN, models are employed as base classifiers. The averaging mechanism is an adaptive voting method. The proposed design has been evaluated using the NSL-KDD dataset, and it has achieved an accuracy of 84.2%, while an adaptive voting mechanism has an accuracy of 85.2%. For improved outcomes, the authors recommend optimizing feature selection and preprocessing. In the [20] research paper, the ensemble-based ML model is proposed using several individual models including RF, GB, and AGX. Bagging and simple stacking averaging techniques are utilized. A port scanning dataset called SIMARG21 has been used and the obtained results illustrate that the RF model has achieved improved performance metrics reaching 99% in accuracy. Ref. [21] provides an ensemble-based model that employs logistic regression, naive Bayes, and a decision tree with a voting classifier. The efficacy of the model was assessed using the CICIDS2017 dataset, which revealed considerable gains in accuracy in binary and multi-class classification scenarios. Ref. [22] presents a cloud-based technique for tasks to fog nodes.