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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Muhammad Rifa'i, Anggi
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Computer Science & IT Engineering

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Optimizing firewall timing for brute force mitigation with random forests Turmudi Zy, Ahmad; Isarianto, Isarianto; Muhammad Rifa'i, Anggi; Ghofir, Abdul; Dwi Miharja, Muhammad Najamuddin; Tri Sasongko, Ananto
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp2945-2954

Abstract

Mitigating brute force attacks remains a critical challenge in cybersecurity, requiring intelligent and adaptive solutions. This research introduces an approach to optimizing firewall deployment timing for enhanced brute force mitigation using pattern recognition techniques with the random forest algorithm. Leveraging the UNSW-NB15 dataset, comprehensive preprocessing and exploratory data analysis (EDA) were performed to ensure the dataset's suitability for machine learning applications. The study utilized a structured workflow, splitting the dataset into training and testing subsets to rigorously evaluate the model's performance. The proposed random forest model achieved a high accuracy of 98.87%, supported by precision, recall, and F1-scores that confirm its effectiveness in distinguishing normal and attack traffic. The confusion matrix further validated the model’s robustness, highlighting its potential in improving the efficiency of firewall deployment. These findings demonstrate the critical role of advanced machine learning techniques in enhancing cybersecurity defenses, particularly in mitigating brute force attacks through optimized, data-driven strategies.
Explainable DDoS Detection with a CNN-LSTM Hybrid Model and SHAP Interpretation Amali, Amali; Muhammad Rifa'i, Anggi; Widodo, Edy; Turmudi Zy, Ahmad; Ariatmanto, Dhani
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025 (in progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.6865

Abstract

The rising frequency and complexity of Distributed Denial of Service (DDoS) attacks pose a severe threat to network security. This study aims to develop an effective and interpretable DDoS detection framework using a hybrid deep learning approach. The proposed method integrates Convolutional Neural Networks (CNN) to capture local traffic patterns and Long Short-Term Memory (LSTM) networks to model temporal dependencies. The CICIDS 2017 dataset, after preprocessing steps including data cleaning, standardization, and class balancing with SMOTE, was used to train and evaluate the model. Experimental results show that the framework achieved 99.98% accuracy and a 99.83% F1-Score, with minimal false positive and false negative rates. This study integrates SHAP to improve model interpretability, aligning feature importance with network security expertise. Future research will focus on real-time deployment, cross-dataset validation, and exploring alternative explainable AI techniques for improved scalability.
Co-Authors Abdul Ghofir Amali, Amali DHANI ARIATMANTO Edy Widodo Isarianto, Isarianto Muhammad Najamuddin Dwi Miharja Tri Sasongko, Ananto Turmudi Zy, Ahmad