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

International Conference on Architectural Support for Programming Languages and Operating Systems - ASPLOS

Volumn , Issue , 2010, Pages 104-110

  Alshawabkeh, Malak ^a   Jang, Byunghyun ^a   Kaeli, David ^a  

^a Northeastern University   (United States)

Author keywords

GPU; Intrusion detection system; LOF; Parallelization

Indexed keywords

ANOMALY DETECTION METHODS; COMPUTATIONAL OVERHEADS; DATA OBJECTS; DUAL-CORE; EXECUTION TIME; FEATURE SPACE; GENERAL-PURPOSE COMPUTING; GPU IMPLEMENTATION; GRAPHICS PROCESSING UNIT; HIGH-LEVEL PROGRAMMING LANGUAGE; INPUT DATAS; INTRUSION DETECTION SYSTEMS; K NEAREST NEIGHBOR ALGORITHM; K-NEAREST-NEIGHBOR QUERIES; LOCAL OUTLIER FACTOR; LOCAL OUTLIER FACTORS; LOCAL OUTLIERS; MACHINE-LEARNING; MULTITHREADED; NEIGHBORHOOD SIZE; PARALLEL COMPUTING; PARALLELIZATIONS;

COMPUTER CRIME; COMPUTER GRAPHICS EQUIPMENT; FUZZY CONTROL; HIGH LEVEL LANGUAGES; LEARNING ALGORITHMS; PARALLEL ARCHITECTURES; PROGRAM PROCESSORS;

INTRUSION DETECTION;

EID: 77952286712     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1735688.1735707     Document Type: Conference Paper

References (16)

1. S. Arya, D. Mount, N. Netanyahu, R. Silverman, and A. Wu. An optimal algorithm for approximate nearest neighbor searching fixed dimensions. J. ACM, 45(6):891-923, 1998.
2. V. Barnett and T. Lewis. Outliers in Statistical Data. John Wiley and Sons Chichester, 3rd edition, 1995.
3. M. M. Breunig, H.-P. Kriegel, R. T. Ng, and J. Sander. Lof: Identifying density-based local outliers. In SIGMOD Conference, pages 93-104, 2000.
4. B. Catanzaro, N. Sundaram, and K. Keutzer. Fast support vector machine training and classification on graphics processors. In ICML '08: Proceedings of the 25th international conference on Machine learning, pages 104-111, 2008.
5. M.-C. Chen, R. Wang, and A.-P. Chen. An Empirical Study for the Detection of Corporate Financial Anomaly Using Outlier Mining Techniques. In ICCIT '07: Proceedings of the 2007 International Conference on Convergence Information Technology, pages 612-617, 2007.
6. S. Ganeriwal, L. Balzano, and M. Srivastava. Reputation-based framework for high integrity sensor networks. ACM Transactions on Sensor Network, 4(3):1-37, 2008.
7. V. Garcia, E. Debreuve, and M. Barlaud. Fast k nearest neighbor search using GPU. In CVPR Workshop on Computer Vision on GPU (CVGPU), June 2008.
8. B. Jang, D. Kaeli, S. Do, and H. Pien. Multi-GPU Implementation of Iterative Tomographic Reconstruction Algorithms. In Biomedical Imaging: From Nano to Macro, 2009. ISBI 2009. 6th IEEE International Symposium on Biomedical Imaging, Jun 2009.
9. B. Jang, P. Mistry, D. Schaa, R. Dominguez, and D. Kaeli. Data transformations enabling loop vectorization on multithreaded data parallel architectures. In Proceedings of the 15th ACM SIGPLAN symposium on Principles and practice of parallel computing, pages 353-354, 2010.
10. A. Lazarevic, L. Ertz, V. Kumar, A. Ozgur, and J. Srivastava. A Comparative Study of Anomaly Detection Schemes in Network Intrusion Detection. In Proceedings of the Third SIAM International Conference on Data Mining, 2003.
11. E. Lozano and E. Acuna. Parallel Algorithms for Distance-Based and Density-Based Outliers. In ICDM '05: Proceedings of the Fifth IEEE International Conference on Data Mining, pages 729-732, 2005. (Pubitemid 47385782)
12. NVIDIA. CUDA Programming Guide 2.3, Jul 2009.
13. NVIDIA. NVIDIA CUDA C Programming Best Practices Guide 2.3, Jul 2009.
14. Snort. The SNORT Network IDS. www.snort.org.
15. D. Steinkraus, I. Buck, and P. Simard. Using GPUs for Machine Learning Algorithms. In Document Analysis and Recognition, Proceedings. Eighth International Conference, 2005.
16. J. Xi. Outlier Detection Algorithms in Data Mining. Intelligent Information Technology Applications, 1:94-97, 2008.