Toward A Holistic, Efficient, Stacking Ensemble Intrusion Detection System using a Real Cloud-based Dataset

Abstract

Network intrusion detection is a key step in securing today's constantly developing networks. Various experiments have been put forward to propose new methods for resisting harmful cyber behaviors. Though, as cyber-attacks turn out to be more complex, the present methodologies fail to adequately solve the problem. Thus, network intrusion detection is now a significant decision-making challenge that requires an effective and intelligent approach. Various machine learning algorithms such as decision trees, neural networks, K nearest neighbor, logistic regression, support vector machine, and Naive Bayes have been utilized to detect anomalies in network traffic. However, such algorithms require adequate datasets to train and evaluate anomaly-based network intrusion detection systems. This paper presents a testbed that could be a model for building real-world datasets, as well as a newly generated dataset, derived from real network traffic, for intrusion detection. To utilize this real dataset, the paper also presents an ensemble intrusion detection model using a meta-classification approach enabled by stacked generalization to address the issue of detection accuracy and false alarm rate in intrusion detection systems.

Cyberpsychology: A Longitudinal Analysis of Cyber Adversarial Tactics and Techniques

“…Post-preprocessing, the cleansed dataset was primed for a comprehensive examination. This examination involved probing for patterns, trends, correlations, and behavioral insights within the data using various analytical instruments and methodologies [14,24,33].…”

“…By adhering to stringent validation protocols, the study aspires to yield reliable and valid findings that can enrich the ongoing cybersecurity discourse and assist in forming more robust and effective cybersecurity strategies [6,11,33].…”