TY - GEN
T1 - Cryptographic Algorithm Identification through Machine Learning for Enhanced Data Security
AU - Rachini, Ali
AU - Abi Assaf, Maroun
AU - Fares, Charbel
AU - Khatoun, Rida
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023/1/1
Y1 - 2023/1/1
N2 - This paper discusses the significance of identifying encryption algorithms in today's digital era to ensure data security. The study uses machine learning (ML) techniques, including Support Vector Machine (SVM), Random Forest, and k-Nearest Neighbors (KNN), to develop a classification model for distinguishing between encryption algorithms like Blowfish, AES, and 3DES. Results show distinct performances among the algorithms, with SVM achieving a robust 91 % accuracy rate, Random Forest excelling in precision with a 99 % accuracy rate, and KNN providing reasonable but comparatively lower accuracy at 34 %. The findings underscore the diverse capabilities of ML algorithms in encryption algorithm identification, offering valuable insights for enhancing data security practices and emphasizing the importance of selecting the most suitable ML approach based on specific security requirements.
AB - This paper discusses the significance of identifying encryption algorithms in today's digital era to ensure data security. The study uses machine learning (ML) techniques, including Support Vector Machine (SVM), Random Forest, and k-Nearest Neighbors (KNN), to develop a classification model for distinguishing between encryption algorithms like Blowfish, AES, and 3DES. Results show distinct performances among the algorithms, with SVM achieving a robust 91 % accuracy rate, Random Forest excelling in precision with a 99 % accuracy rate, and KNN providing reasonable but comparatively lower accuracy at 34 %. The findings underscore the diverse capabilities of ML algorithms in encryption algorithm identification, offering valuable insights for enhancing data security practices and emphasizing the importance of selecting the most suitable ML approach based on specific security requirements.
KW - Data Security
KW - Encryption Algorithm De-tection
KW - Machine Learning
KW - Symmetric Algorithm
U2 - 10.1109/EICEEAI60672.2023.10590185
DO - 10.1109/EICEEAI60672.2023.10590185
M3 - Conference contribution
AN - SCOPUS:85199983606
T3 - 2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023
BT - 2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023
Y2 - 27 December 2023 through 28 December 2023
ER -