# Evaluating Ensemble Learning Mechanisms for Predicting Advanced Cyber Attacks
## 1 Introduction
### Research Objectives and Contributions  
Ensemble machine learning techniques, which leverage the diversity and complementary strengths of multiple models, have the potential to significantly improve the accuracy, efficiency, and robustness of NIDS for detecting known and unknown attacks. We hypothesize that an ensemble approach, which combines multiple machine learning algorithms or models, will outperform a single-model approach in terms of detection accuracy, false positive rate, and detection time, across different types of network attacks scenarios. Thishypothesis emphasizes the advantages of using an ensemble approach for NIDS, which include the ability to handle complex and dynamic attack patterns, mitigate the impact of noisy or conflicting data, and adapt to evolving threats. It also highlights the key evaluation metrics that would be used to test the hypothesis, such as accuracy (the ability to correctly classify benign and malicious traffic), false positive rate (the frequency of misclassifying benign traffic as malicious), and detection time (the speed of identifying and reporting attacks). The hypothesis could be validated through empirical experiments that compare the performance of different ensemble techniques with that of a baseline single-model approach on benchmark datasets or real-world traffic traces. The primary goal of this research paper is to evaluate the performance of ensemble machine learning techniques in the analysis of Network Intrusion Detection systems (NIDS) data. The predictive models created by different algorithms are utilized to recognize sophisticated cyber-attacks and advanced persistent threats (APT). The following specific objectives will be tackled:  
* Examining the current state of NIDS systems and machine learning for cyber-attack prediction. The application of ensemble machine learning to NIDS is reviewed to identify the limitations and challenges that need to be addressed. * Assessing and contrasting various machine learning algorithms for predicting cyber-attacks using different NIDS datasets. * Applying various stand-alone and ensemble machine learning mechanisms to develop and implement a system as a predictive model for the detection of unknown and advanced cyber intrusions. * Selecting a realistic dataset that reflects real-world network traffic. The dataset contains advanced attack and APT patterns in a spatial-temporal distribution. * Conducting a feature engineering process to extract the most effective attributes from the dataset to provide an accurate prediction. * Evaluation of the efficacy of the proposed system using different performance criteria. Despite the existence of few research efforts to evaluate ensemble learning approaches in several applications, there is a demand for more technical studies using distinct ensemble mechanisms, different tuning parameters, and various collections of ML algorithms. The study's contributions to the network security field can be presented in the following:  
* Implementation and evaluation of distinct ensemble machine learning techniques for improving attack detection in Network Intrusion Detection Systems (NIDS). * Development and implementation of different structures of ensemble-based NIDS trained in real-world attacks. * Investigation of the impact of different ensemble techniques, including stacking, boosting, and bagging, on the detection accuracy, false positive rate, and detection time of NIDS. * Evaluation of the proposed system using a well-known and documented benchmark dataset:
* In terms of popularity and usage, the KDD Cup 1999 [7] dataset is one of the most widely used datasets for evaluating intrusion detection systems. However, this dataset is quite old (it was released in 1999) and may not reflect the current landscape of network attacks and traffic. Moreover, the NSL-KDD dataset [9], which is a modified version of the KDD Cup 1999 dataset, is also quite popular and has been used in many studies.