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All Journal Community Service Journal of Indonesia Jurnal Teknik Informatika (JUTIF)
Aziz Bukhori, Hilmi
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Computer Science & IT Health Professions Medicine & Pharmacology Nursing Public Health

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Improving Lateral-Movement Intrusion Detection in Virtualized Networks using SHAP Feature Selection, SMOTE, and a Voting Ensemble Classifier Maulana, Avin; Anam, Syaiful; Aziz Bukhori, Hilmi
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.5233

Abstract

Modern virtualized networks, such as those using VXLAN (Virtual eXtensible LAN), generate heavy east–west traffic, which can conceal the lateral movement of attackers. Detecting such infiltration attacks is challenging due to overlay encapsulation (e.g., VXLAN) and flat subnet architectures create blind spots for traditional IDS.  This study aims to evaluate a robust methodology for addressing class imbalance in intrusion detection by integrating SHAP-driven feature selection with SMOTE in a voting ensemble. We conducted an ablation study on the CICIDS2017 Thursday-WorkingHours-Afternoon-Infiltration subset, which is highly imbalanced (36 infiltration flows vs. 288,566 benign flows), varying SHAP feature sets (Top-5 vs. Top-30), classification thresholds , and SMOTE (Synthetic Minority Over-sampling Technique) balancing. The ensemble combined XGBoost, Random Forest, and Logistic Regression, and was evaluated with ROC-AUC, precision, recall, and F1-score. Results indicate that using more SHAP‑important features improves ROC‑AUC and recall, while SMOTE substantially enhances minority‑class detection. The best configuration is Top‑30 SHAP features with SMOTE at , achieved ROC‑AUC = 0.976 and F1‑score = 0.78, whereas using fewer features or omitting SMOTE significantly reduced recall and F1‑score. This synergy of interpretable feature selection and synthetic oversampling establishes a practical methodology for intrusion detection in highly imbalanced, modern virtualized environments. The novelty lies in demonstrating that SHAP + SMOTE integration yields both transparency and resilience, directly addressing encapsulation challenges in detecting stealthy lateral movement.
IMPROVING MADRASAH TEACHERS' COMPETENCIES IN ARTIFICIAL INTELLIGENCE-BASED LEARNING DATA PROCESSING IN BATU CITY Anam, Syaiful; Aziz Bukhori, Hilmi; Maulana, Avin; Yanti, Indah; Gustiningsih Hapsani, Anggi
Community Service Journal of Indonesia Vol. 7 No. 2 (2025): Community Service Journal of Indonesia
Publisher : Institute for Research and Community Service, Health Polytechnic of Kerta Cendekia, Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36720/csji.v7i2.813

Abstract

Teacher competence is a crucial factor in improving the quality of education..This community service program aimed to enhance the professional competence of madrasah teachers in processing learning data using artificial intelligence (AI)-based tools. Conducted through a one-day intensive workshop in Batu City, the program involved 18 teachers from five madrasahs at the Madrasah Aliyah and Madrasah Tsanawiyah levels. The training adopted the ADDIE instructional design model, covering needs analysis, AI-assisted data processing with Google Sheets and ChatGPT/OpenAI, reinforcement of AI ethics, and infographic creation. Quantitative evaluation showed a significant improvement in participants’ competencies, with average scores increasing from 60.3 (pre-test) to 86.3 (post-test). The most notable progress was observed in logical operations (IF function mastery) and ethical awareness in AI use, while 88% of participants reported high satisfaction with the training content and delivery. The program effectively integrated digital literacy, ethical reflection, and practical application to foster teacher professionalism. Beyond individual competence, this initiative contributed to building a sustainable collaborative network through the Subject Teachers’ Working Group (MGMP) and provided a replicable model for technology-based professional development in Islamic education.
Co-Authors Gustiningsih Hapsani, Anggi Indah Yanti Maulana, Avin Syaiful Anam