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All Journal Journal of Embedded Systems, Security and Intelligent Systems
Hartinah
State Polytechnic of Ujung Pandang
Computer Science & IT

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COMPASS: Comparative Evaluation of Machine Learning Algorithms for DDoS Detection Using ANOVA F-Value on AISED Dataset Hartinah; Irfan Syamsuddin; Andi Syarwani
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 2 (2025): June 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i2.8276

Abstract

This study presents COMPASS, a comparative evaluation of ten Machine Learning algorithms for DDoS attack detection using the AISED Dataset on Cloud DDoS Attacks. Feature selection was performed using SelectKBest with ANOVA F-Value, evaluating model performance across varying feature dimensions (K = 10, 15, 20, 25). Experimental results demonstrate that ensemble-based methods, particularly Random Forest, Gradient Boosting, and AdaBoost, achieve near-theoretical maximum AUC scores (>0.998) while maintaining fast training times (<0.1 seconds). K-Nearest Neighbors (KNN) also exhibits robust performance (AUC > 0.98) with minimal computational cost. In contrast, Support Vector Machine (SVM) and Quadratic Discriminant Analysis (QDA) show relatively lower accuracy (AUC > 0.85) and suffer from high computational complexity, with SVM requiring up to 572 seconds to train at K=25. These findings highlight the critical trade-off between classification accuracy and computational efficiency in selecting optimal models for real-time DDoS detection systems. As future work, we propose deploying a lightweight version of COMPASS on edge computing devices and integrating it into federated learning frameworks to enable collaborative, privacy preserving model training.
Optimized Automated Virtual Private Network Management System Utilizing WireGuard and Redis with Delta Processing on MikroTik RouterOS Ardiansyah; Hartinah; Wahyuddin Saputra
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 3 (2025): September 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i3.10044

Abstract

In the contemporary network infrastructures, the pursuit of secure, efficient, and easily manageable connectivity has become a fundamental design requirement. WireGuard, a next-generation Virtual Private Network (VPN) protocol, delivers superior cryptographic efficiency and operational performance compared to traditional protocols such as OpenVPN and IPsec. This study presents the development of an optimized automated VPN management framework built upon WireGuard and integrated into MikroTik RouterOS via a web-based control interface. The proposed system automates user registration, account approval, and configuration generation processes, thereby reducing administrative overhead and minimizing human-induced configuration errors. To further enhance system performance, an optimized traffic logging mechanism was implemented using the Redis in-memory database combined with a Delta Processing algorithm, which records only incremental traffic variations instead of cumulative totals. Experimental validation demonstrates that this integration reduces router CPU utilization by 30% and decreases logging latency by 40% relative to conventional polling-based methods. The results confirm that the proposed solution not only achieves full automation of VPN management but also significantly improves the responsiveness and scalability of real-time traffic monitoring. To the best of our knowledge, this research introduces the first Redis-based Delta Processing integration for VPN optimization on MikroTik platforms, offering a lightweight, scalable, and high-throughput solution for multiuser network environments.
Co-Authors Andi Syarwani Ardiansyah Irfan Syamsuddin Wahyuddin Saputra