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Bima Aditya
Universitas Royal
Computer Science & IT Economics, Econometrics & Finance Education Social Sciences

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SELEKSI FITUR MENGGUNAKAN MUTUAL INFORMATION UNTUK DETEKSI INTRUSI Riki Andri Yusda; Sahren Sahren; Mustika Fitri Larasati Sibuea; Nadira Meutia Arifin; Bima Aditya
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 8 No. 3 (2025): August 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i3.3112

Abstract

Abstract: This study explores the use of Mutual Information (MI) for feature selection in intrusion detection, focusing on the CICIDS 2017 dataset. Given the complexity and large volume of data in intrusion detection systems, this research aims to identify the most informative features. The methodology includes data preprocessing, MI calculation, and feature selection based on the highest MI values. The analysis results indicate that using MI contributes to improving model accuracy and reducing the false positive rate. These findings underscore the importance of feature selection in enhancing the effectiveness of intrusion detection systems and provide significant contributions to developing more efficient cybersecurity strategies. Keyword: IDS, Mutual Information, CICIDS2017, Feature selection Abstrak: Penelitian ini mengeksplorasi penggunaan Mutual Information (MI) untuk seleksi fitur dalam deteksi intrusi, dengan fokus pada dataset CICIDS 2017. Mengingat kompleksitas dan volume data yang besar dalam sistem deteksi intrusi, penelitian ini bertujuan untuk mengidentifikasi fitur-fitur yang paling informatif. Metodologi yang diterapkan mencakup preprocessing data, perhitungan MI, dan seleksi fitur berdasarkan nilai MI tertinggi. Hasil analisis menunjukkan bahwa penggunaan MI berkontribusi pada peningkatan akurasi model serta pengurangan tingkat false positive. Temuan ini menegaskan pentingnya seleksi fitur dalam meningkatkan efektivitas sistem deteksi intrusi dan memberikan kontribusi signifikan dalam pengembangan strategi keamanan siber yang lebih efisien. Kata kunci: IDS, Mutual Information, CICIDS2017, Seleksi fitur
DETEKSI ZERO-DAY SOCIAL ENGINERING ATTACK MENGGUNAKAN NLP DAN OPEN-SET DEEP LEARNING Sahren Sahren; Ruri Ashari Dalimunthe; Bima Aditya
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 2 (2026): April 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i2.6120

Abstract

Text based social engineering attacks are a growing cyber threat that is difficult to detect by conventional intrusion detection systems, especially in previously unobserved or zero-day variants. This study proposes a Natural Language Processing Open-Set Intrusion Detection System (NLP-OSIDS) framework that integrates Term Frequency-Inverse Document Frequency (TF-IDF) trigram (1.3-gram) feature representation with an Open-Set Multilayer Perceptron architecture based on energy based scoring to detect zero-day social engineering attacks without requiring training examples from that class. Experiments were conducted on the public dataset phishing_email.csv with 82,486 combined samples from Enron, SpamAssassin, Nazario, Ling, CEAS, and Nigerian Fraud datasets with strict zero-day partitioning following open-set recognition evaluation standards. The results show that NLP-OSIDS achieved an AUROC of 0.7808, surpassing all closed-set baselines (AUROC = 0.500) with the lowest False Positive Rate of 0.0088, while the Zero-Day Detection Rate (ZD-DR) of 0.077 indicates the need for adaptive threshold optimization as a direction for further research.
Co-Authors Mustika Fitri Larasati Sibuea Nadira Meutia Arifin Riki Andri Yusda Ruri Ashari Dalimunthe sahren, sahren