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Universitas Muhammadiyah Riau
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Klasifikasi Algoritma Kriptografi pada Pesan Terenkripsi menggunakan Support Vector Machine (SVM) Fatma, Yulia; Gunawan, Rahmad; Nurkhairi Fitri; Firdaus, Rahmad; Hayami, Regiolina; Soni, Soni
JURNAL FASILKOM Vol. 15 No. 3 (2025): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v15i3.10843

Abstract

Data protection has become a highly critical aspect, particularly in addressing ransomware threats that illegally encrypt data. This study is important to evaluate the capability of machine learning techniques in identifying encryption algorithms used in encrypted data, especially in ransomware attacks. This work represents an initial step that can assist cybersecurity practitioners in more rapidly understanding attack patterns, determining appropriate response strategies, and enhancing proactive mitigation and response efforts to protect data against increasingly complex cyber threats. The machine learning algorithm employed in this study is the Support Vector Machine (SVM). The dataset consists of ciphertext generated using the AES, DES, and Vigenère Cipher cryptographic algorithms. The feature extraction process utilizes ten statistical features to capture the distinctive patterns of each type of ciphertext. The SVM model is developed using a data split of 90% for training and 10% for testing. Performance evaluation is conducted using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The result demonstrate an average accuracy 0f 92,33%, with the vigenere cipher being perfectly classified (100% accuracy). Howefer, slight misclassifications occured beetween AES and DES duet o their similiar entropy chraracteristic. Experimental results demonstrate that the SVM model is capable of identifying encryption algorithms with high accuracy and balanced classification performance across the three algorithm classes. These findings highlight the potential of machine learning approaches for detecting encryption algorithms in cyber-attacks, thereby making a meaningful contribution to the improvement of proactive data security mitigation and response strategies.
Deteksi Serangan Dalam Ekosistem Iot Melalui Analisis Multi-Class Dengan Model Xgboost Dan Penerapan Teknik Imbalance Ratio Pada Dataset IoTID20 Amien, Januar Al; Sunanto, Sunanto; Rangkuti, Muhammad Al-Ikhsan; Soni, Soni
Computer Science and Information Technology Vol 6 No 3 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v6i3.9861

Abstract

This research focuses on attack detection in the Internet of Things (IoT) ecosystem using the XGBoost algorithm and the Imbalance Ratio technique on the IoTID20 dataset. The main goal is to overcome the problem of data imbalance that is common in IDS datasets and improve accuracy in classifying attack types. The methodology used includes data preprocessing, feature selection, and applying the Imbalance Ratio technique to handle class imbalance in the IoTID20 dataset. Next, the XGBoost model is implemented with the scale_pos_weight parameter to handle the class imbalance problem. This model is trained on training data and evaluated using metrics such as accuracy, precision, recall, and F1-score. The research results show that the combination of the XGBoost algorithm and the Imbalance Ratio technique is able to overcome data imbalance problems effectively. The resulting model achieved an accuracy rate of 99.32%, precision 99.32%, recall 99.32%, and F1-score 99.32% in classifying attack types on the IoTID20 dataset. These results demonstrate excellent capabilities in detecting attacks and distinguishing between normal and anomalous traffic in the IoT ecosystem. This research contributes to improving IoT network security by applying an effective Machine Learning approach to accurately detect attacks, while also addressing data imbalance problems that often occur in IDS datasets.
Co-Authors Ab Ghani, Hadhrami Agusriadi, - Al Amien, Januar Alris Gusnanda Aminullah, Rabiah Aminuyati Amran, Hasanatul Fu'adah Anam, M Khairul Ananda Fitria Andesa, Khusaeri ANDRIANSYAH Arie Zella Putra Ulni Arkan, M Alif Baidarus Bambang Sugiantoro Bayu Anugerah Putra Br Bangun, Elsi Titasari Dasrizal Daud, Kauthar Mohd Deprizon, Deprizon Desti Mualfah Deyola Shifana Diah Angraina Fitri Diah Angraini Putri Dian Utami Didik Sudyana Edi Ismanto Eka Putra Eka Ramadhan Elsa Yuniarti Erna M.Si Juita S.Pd Evans Fuad Fakhira Frisya Ramadhani Falda Dimantara Fatma, Yulia Febby Apri Wenando Fitri Handayani Fitria Aini, Fitria Fransiskus Zoromi, Fransiskus Gunawan, Rahmad Hadi Nasbey Hafid, Afdhil Hanum Salsabila Hari Sepdian Harun Mukhtar Hasanuddin Hasanuddin Hayami, Regiolina Hendri, Yusriadi Herianto Herianto Hul Hasanah, Sifa Ibrahim, Mohd Hairy Ilham Firdaus Irzi Gunawan Januar Al Amien Januar Al Amien Jihan Aulia Kultum, Fi Ardhi Laksono Trisnantoro Lisman, Muhammad Mas’yuri, Dhina Nurriska Md Saleh, Nurul Izrin Miftakhul Jannah Mikdad Amseno Mohamad, Mohd Saberi Mohd Daud, Kauthar Muhammad Fajri Jamil Muhammad Hamadi Muzahaffar, Fatih Al Nengsih, Rafni Yulia Nurkhairi Fitri Prastiwi, Adila Pramudiah Putra, Reza Tanujiwa Rahmad Firdaus Rahmad Firdaus Rahmaddeni Rahmaddeni Ramadhanti, Nurul Randra Aguslan Pratama Rangkuti, Muhammad Al-Ikhsan Remli, Muhammad Akmal Reny Medikawati Taufik Ricinur Ricinur Rico Apriandika Ridhollah, Farhan Rinaldi Rinaldi Rizki Anwar Rizki, Yoze Rizky Rahman Salam Septiana Srinandini Sofhia Mohnica Sunanto Sunanto Sy, Yandiko Saputra Torkis Nasution Unik, Mitra Vanama, Melsa Wan Salihin Wong, Khairul Nizar Syazwan Yogi Alfinaldo Yoze Rizki Yudi Prayudi Yulia Fatma Yulia Fatma Yusril Ibrahim