Fast intrusion detection based on a non-negative matrix factorization model

In this paper, we present an efficient fast anomaly intrusion detection model incorporating a large amount of data from various data sources. A novel method based on non-negative matrix factorization (NMF) is presented to profile program and user behaviors of a computer system. A large amount of high-dimensional data is collected in our experiments and divided into smaller data blocks by a specific scheme. The system call data is divided into blocks by processes, while command data is divided into consecutive blocks with a fixed length. The frequencies of individual elements in each block of data are computed and placed column by column as data vectors to construct a matrix representation. NMF is employed to reduce the high-dimensional data vectors and anomaly detection can be realized as a very simple classifier in low dimensions. Experimental results show that the model presented in this paper is promising in terms of detection accuracy, computation efficiency and implementation for fast intrusion detection.