Accelerating the local outlier factor algorithm on a GPU for intrusion detection systems

The Local Outlier Factor (LOF) is a very powerful anomaly detection method available in machine learning and classification. The algorithm defines the notion of local outlier in which the degree to which an object is outlying is dependent on the density of its local neighborhood, and each object can be assigned an LOF which represents the likelihood of that object being an outlier. Although this concept of a local outlier is a useful one, the computation of LOF values for every data object requires a large number of k-nearest neighbor queries – this overhead can limit the use of LOF due to the computational overhead involved. Due to the growing popularity of Graphics Processing Units (GPU) in general-purpose computing domains, and equipped with a high-level programming language designed specifically for general-purpose applications (e.g., CUDA), we look to apply this parallel computing approach to accelerate LOF. In this paper we explore how to utilize a CUDA-based GPU implementa...

• Type: Conference
• Year: 2010
• Where: ASPLOS
• Authors: Malak Alshawabkeh, Byunghyun Jang, David R. Kaeli
• Keywords: ASPLOS 2010, K-nearest Neighbor, Local Outlier, Local Outlier Factor, Programming Languages