Ensemble-Based Predictive Model for Cyber Attack Detection: Development and Evaluation

In an era of pervasive digital connectivity, cyber-attacks have become increasingly sophisticated, persistent, and difficult to detect using conventional security mechanisms. Traditional intrusion detection systems (IDS) often rely on single classifier models, which tend to underperform when faced with complex, dynamic, and high-dimensional network data. This research proposes an ensemble-based predictive model for cyber attack detection that integrates multiple machine learning algorithms to enhance detection accuracy, robustness, and generalization. The model employs a hybrid ensemble strategy combining bagging and boosting techniques, utilizing algorithms such as Random Forest, Gradient Boosting, and Support Vector Machines (SVM) to leverage the strengths of diverse learners while minimizing their individual weaknesses.

The study utilizes benchmark cybersecurity datasets such as NSL-KDD and CICIDS2017, which encompass a wide range of network intrusions including Denial-of-Service (DoS), Probe, R2L, and U2R attacks. Data preprocessing techniques—comprising feature encoding, normalization, and dimensionality reduction—are applied to ensure optimal learning conditions and minimize noise interference. The ensemble model is trained and evaluated using performance metrics including accuracy, precision, recall, F1-score, false positive rate (FPR), and ROC-AUC to measure both detection efficiency and model reliability.

Experimental results demonstrate that the ensemble-based model significantly outperforms individual classifiers in identifying both known and zero-day attacks. The proposed system achieves high detection accuracy while maintaining a low false positive rate, which is critical for real-world cybersecurity applications. The hybrid ensemble approach proves effective in addressing data imbalance, model overfitting, and classification bias commonly associated with standalone models. Moreover, the evaluation results indicate that ensemble learning enhances decision stability and adaptability in detecting evolving attack patterns.

The research concludes that ensemble-based predictive modelling offers a scalable, reliable, and intelligent framework for next-generation intrusion detection systems (IDS). The findings underscore the importance of integrating multiple learning paradigms to build resilient cybersecurity infrastructures. Future research is recommended to explore deep ensemble learning, real-time adaptive learning systems, and integration with cloud-based security architectures to further improve predictive performance and operational scalability in dynamic network environments.