Comparison of Selected Machine Learning Techniques in Cyberattack Anomaly Detection

• Author(s): Dorcas Atinuke Adedokun ; Wasiu Oladimeji Ismaila ; Simeon Ayoade Adedokun ; Elizabeth A. Amusan ; Folasade Muibat Ismaila
• Paper ID: 1711113
• Page: 342-353
• Published Date: 08-10-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 4 October-2025

Abstract

The digital age has ushered in unprecedented connectivity and technological advancement, which have also introduced a surge in sophisticated and frequent cyber threats. To safeguard systems, anomaly detection has become a cornerstone of cybersecurity, enabling the identification of deviations from normal system behaviour. This study presents a comparative analysis of three machine learning techniques—Isolation Forest, Long Short-Term Memory (LSTM), and Q-Learning—for cyberattack anomaly detection. The study designed and implemented a system using the CICIDS-2017 dataset (2,830,743 records) in Python, preceded by data preprocessing and feature engineering. Evaluation metrics, including Accuracy, F1-Score, and error rates (FPR, FNR) revealed a clear performance hierarchy. The LSTM model proved superior, achieving a near-perfect Accuracy of 99.53% with minimal errors (FPR: 0.35%, FNR: 0.50%). Q-Learning showed strong, adaptive potential, recording an Accuracy of 92.80% and an F1-Score of 90.25%, though with higher error rates (FPR: 8.58%). Conversely, the unsupervised Isolation Forest was inadequate for this labeled task, with metrics around 50%. The findings establish LSTM as ideal for maximum accuracy, Q-Learning as a viable option for dynamic environments, and highlight the limitations of simple unsupervised methods on complex security datasets.

Keywords

Cyberattack, Anomaly, Detection, Machine, Learning, Isolation Forest, Q-Learning, LSTM, Long Short-Term, Memory.

Citations

IRE Journals:
Dorcas Atinuke Adedokun , Wasiu Oladimeji Ismaila , Simeon Ayoade Adedokun , Elizabeth A. Amusan , Folasade Muibat Ismaila "Comparison of Selected Machine Learning Techniques in Cyberattack Anomaly Detection" Iconic Research And Engineering Journals Volume 9 Issue 4 2025 Page 342-353

IEEE:
Dorcas Atinuke Adedokun , Wasiu Oladimeji Ismaila , Simeon Ayoade Adedokun , Elizabeth A. Amusan , Folasade Muibat Ismaila "Comparison of Selected Machine Learning Techniques in Cyberattack Anomaly Detection" Iconic Research And Engineering Journals, 9(4)