# Evaluating Ensemble Learning Mechanisms for Predicting Advanced Cyber Attacks
## 1 Introduction  
### Background  
In today's technologically advanced world, computer networks have become integral to our daily activities. However, this growing dependence on networks has also given rise to a surge in cyber-attacks. While security defenses like intrusion detection systems (IDS), firewalls, and anti-malware software play a crucial role in identifying attacks, their effectiveness largely relies on the detection models they employ. Unfortunately, conventional mechanisms often fall short in detecting advanced and sophisticated attacks, necessitating the integration of intelligent models. To combat these emerging threats, the use of machine learning, data analytics, and threat intelligence has become essential. By harnessing the power of these technologies, hidden relationships among intrusion events can be extracted, potential vulnerabilities identified, and indicators of compromise detected. This enables proactive measures to anticipate and prevent cyber-attacks before they occur [1]. In network security infrastructure, NIDS have emerged as vital components. NIDS are designed to monitor and analyze network traffic to identify suspicious and malicious activities. To enhance the accuracy and efficacy of NIDS, machine learning algorithms have proven to be essential to create intelligent security mechanisms [2]. One such powerful technique is ensemble machine learning, which combines multiple homogenous or heterogeneous models to achieve superior detection performance. The potential of machine learning to enhance the accuracy and responsiveness of NIDS is immense [3]. By leveraging historical data, these models can effectively learn patterns of normal and anomalous network behavior, enabling them to classify incoming traffic as either benign or potentially malicious. This real-time detection capability empowers security teams to respond swiftly to potential attacks, thereby preventing or mitigating their impact [4]. Ultimately, integrating machine learning with NIDS can significantly sustain network security and reduce the risk of cyber-attacks. Ensemble machine learning has attracted significant attention in research due to its ability to create more robust and accurate systems for attack detection [5]. This approach addresses the limitations associated with individual algorithms, such as overfitting, bias, or poor generalization of new data. By combining multiple classifiers, ensemble learning consistently outperforms individual models, resulting in enhanced accuracy [6]. The collaborative nature of ensemble methods can be classified into three categories: bagging, boosting, and stacking. Bagging involves training multiple models on different input subset samples and averaging their predictions, effectively reducing variance and addressing overfitting [7]. Boosting, on the other hand, integrates multiple weak learners, each learning from the mistakes of its predecessors, resulting in a stronger learner capable of making improved predictions and reducing bias in the training set [8]. Stacking, a two-level ensemble learning technique, combines the predictions of different single learning algorithms to make more accurate predictions on new data [8]. Various combination methods of ensemble algorithms including bagging, stacking, and boosting can be configured and designed to improve the model performance. It is also essential for leveraging the strengths and capacities of individual algorithms employed in different settings. ML algorithms offer a distinct set of characteristics and operational efficiencies in different contexts with diverse dimensionalities. Ensemble learning entails the combination of these available capacities, resulting in a system with precise prediction of potential unknown attacks. Moreover, limitations associated with a certain algorithm can be avoided by facilitating the efficiency features of another algorithm.