Intrusion Detection Based on Behavior Mining and Machine Learning Techniques
作者： Srinivas Mukkamala , Dennis Xu , Andrew H. Sung
DOI: 10.1007/11779568_67
关键词:
摘要: This paper describes results concerning the classification capability of unsupervised and supervised machine learning techniques in detecting intrusions using network audit trails. In this we investigate well known techniques: Frequent Pattern Tree mining (FP-tree), regression tress (CART), multivariate splines (MARS) TreeNet. The best model is chosen based on accuracy (ROC curve analysis). show that high accuracies can be achieved a fraction time required by support vector machines artificial neural networks. TreeNet performs for normal, probe denial service attacks (DoS). CART user to super (U2su) remote local (R2L).
springer.com
sci-hub.st 参考文章(16)
Yihua Liao, V. Rao Vemuri, Wenjie Hu, Robust Support Vector Machines for Anomaly Detection in Computer Security. international conference on machine learning and applications. pp. 168- 174 ,(2003)
Levent Ertöz, Aleksandar Lazarevic, Vipin Kumar, Jaideep Srivastava, Aysel Ozgur, A Comparative Study of Anomaly Detection Schemes in Network Intrusion Detection. siam international conference on data mining. pp. 25- 36 ,(2003)
Seth E. Webster, The development and analysis of intrusion detection algorithms Massachusetts Institute of Technology. ,(1998)
Angelos D. Keromytis, Krysta Svore, Salvatore Stolfo, Katherine Heller, One Class Support Vector Machines for Detecting Anomalous Windows Registry Accesses Workshop on Data Mining for Computer Security (DMSEC), Melbourne, FL, November 19, 2003. pp. 2- 9 ,(2003) , 10.7916/D84B39Q0
James Franklin, The elements of statistical learning : data mining, inference,and prediction The Mathematical Intelligencer. ,vol. 27, pp. 83- 85 ,(2005) , 10.1007/BF02985802
Richard A Olshen, Charles J Stone, Leo Breiman, Jerome H Friedman, Classification and regression trees ,(1983)
MIKE FUGATE, JAMES R. GATTIKER, COMPUTER INTRUSION DETECTION WITH CLASSIFICATION AND ANOMALY DETECTION, USING SVMs International Journal of Pattern Recognition and Artificial Intelligence. ,vol. 17, pp. 441- 458 ,(2003) , 10.1142/S0218001403002459
Jiawei Han, Jian Pei, Yiwen Yin, Mining frequent patterns without candidate generation international conference on management of data. ,vol. 29, pp. 1- 12 ,(2000) , 10.1145/335191.335372
Jerome H. Friedman, Stochastic gradient boosting Computational Statistics & Data Analysis. ,vol. 38, pp. 367- 378 ,(2002) , 10.1016/S0167-9473(01)00065-2
Wenke Lee, Salvatore J. Stolfo, A framework for constructing features and models for intrusion detection systems ACM Transactions on Information and System Security. ,vol. 3, pp. 227- 261 ,(2000) , 10.1145/382912.382914