Cite this Article
Tarun Kumar Ahirwar, Paulami Sahu, Prashant V. Baredar. Preliminary CFD Anomaly Identification in Cyber Logs Through Machine Learning Techniques. International Research Journal of Engineering & Applied Sciences (IRJEAS). 12(4), pp. 01-11, 2025. https://doi.org/10.55083/irjeas.2025.v13i04001
Abstract:
This paper presents a preliminary computational fluid dynamics (CFD) assessment of four conical frustum windconcentrator configurations (Models A–D) intended for augmentation of small wind turbines in low-wind-speed regions. The designs share a fixed 3:1 inlet-to-outlet area ratio and differ only in frustum length and wall angle, providing a controlled comparison of geometric effects. Steady Reynolds-averaged Navier–Stokes (RANS) simulations with the Realizable k-ε model in ANSYS Fluent quantify outlet velocity amplification (V_Amp = V_out / V_in), radial uniformity and turbulence kinetic energy (TKE). Model B (short and steep taper) achieves the highest V_Amp, whereas longer designs (Models C and D) yield more uniform outflow at the expense of reduced gain. Model A strikes an intermediate balance. The results establish clear geometry–performance trade-offs that are practically relevant: overly high tapers risk non-uniform, turbulent outflow, while long ducts add material and drag but offer diminishing returns. This paper includes only the preliminary interpretation derived from test-run CFD observations. These CFD trends form the basis for turbine integrated modeling and controlled experimentation in subsequent work.
Cite this Article
Ram Dwivedi, Nitesh Gupta, Anurag Shrivastava. Anomaly Identification in Cyber Logs Through Machine Learning Techniques. International Research Journal of Engineering & Applied Sciences (IRJEAS). 12(4), pp. 12-20, 2025. https://doi.org/10.55083/irjeas.2025.v13i04002
Abstract:
With the rising volume and sophistication of cyber attacks, traditional rule-based security systems have become insufficient to detect emerging and unknown threats. Machine Learning (ML) provides a dynamic and scalable solution for anomaly detection in cyber security logs, enabling systems to identify unusual patterns with minimal human intervention. This study offers a thorough framework that combines supervised, unsupervised, and semi-supervised models for anomaly identification using machine learning.
Using the HDFS log dataset, we implement and evaluate Isolation Forest, Auto encoder, and Random Forest algorithms. The results demonstrate that ML techniques significantly enhance detection accuracy and reduce false positives, with Random Forest achieving the highest performance across all evaluation metrics.