Open AccessNetwork intrusion detection method based on matrix factorization of their time and frequency representations
Author(s) -
Chountasis Spiros,
Pappas Dimitrios,
Sklavounos Dimitris
Publication year - 2021
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.2019-0476
Subject(s) - singular value decomposition , intrusion detection system , robustness (evolution) , computer science , matrix decomposition , data mining , factorization , algorithm , non negative matrix factorization , matrix (chemical analysis) , fourier transform , principal component analysis , representation (politics) , scheme (mathematics) , network security , pattern recognition (psychology) , artificial intelligence , mathematics , materials science , law , mathematical analysis , chemistry , composite material , operating system , biochemistry , political science , physics , politics , gene , quantum mechanics , eigenvalues and eigenvectors
In the last few years, detection has become a powerful methodology for network protection and security. This paper presents a new detection scheme for data recorded over a computer network. This approach is applicable to the broad scientific field of information security, including intrusion detection and prevention. The proposed method employs bidimensional (time‐frequency) data representations of the forms of the short‐time Fourier transform, as well as the Wigner distribution. Moreover, the method applies matrix factorization using singular value decomposition and principal component analysis of the two‐dimensional data representation matrices to detect intrusions. The current scheme was evaluated using numerous tests on network activities, which were recorded and presented in the KDD‐NSL and UNSW‐NB15 datasets. The efficiency and robustness of the technique have been experimentally proved.