Klasifikasi Algoritma Kriptografi pada Pesan Terenkripsi menggunakan Support Vector Machine (SVM)

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.