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http://www.cs.unc.edu/~jeffay/courses/nidsS05/ai/robust-anomaly-detection-using.pdf
MIT’s Lincoln Labs to study intrusion detection systems, the performance of robust support vector machines (RSVMs) was compared with that of conventional support vector machines and nearest neighbor classifiers in separating normal usage profiles from intrusive profiles of computer programs. The results
https://www.researchgate.net/publication/2890287_Robust_Anomaly_Detection_Using_Support_Vector_Machines
Using the 1998 DARPA BSM data set collected at MIT's Lincoln Labs to study intrusion detection systems, the performance of robust support vector machines (RSVMs) was compared with that of ...
https://web.cs.ucdavis.edu/~vemuri/papers/rvsm.pdf
In this paper, we present a new approach, based on Robust Support Vector Machines (RSVMs) [9], to anomaly detection over noisy data. RSVMs effectively address the over-fitting problem introduced by the noise in the training data set. With RSVMs, the incorporation of an averaging technique in the standard support vector machines makes the decision surface
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.2.6527
Using the 1998 DARPA BSM data set collected at MIT's Lincoln Labs to study intrusion detection systems, the performance of robust support vector machines (RSVMs) was compared with that of conventional support vector machines and nearest neighbor classifiers in separating normal usage profiles from intrusive profiles of computer programs.
https://www.researchgate.net/publication/221226770_Robust_Support_Vector_Machines_for_Anomaly_Detection_in_Computer_Security
Using the 1998 DARPA BSM data set collected at MIT's Lincoln Labs to study intrusion detection systems, the performance of robust support vector machines (RVSMs) was compared with that of ...
https://link.springer.com/chapter/10.1007%2F978-3-540-28647-9_97
Abstract. In anomaly detection, we record the sequences of system calls in normal usage, and detect deviations from them as anomalies. In this paper, one-class support vector machine(SVM) classifiers with string kernels are adopted as the anomaly detector.Cited by: 6
https://www.sciencedirect.com/science/article/pii/S0925231218305666
Anomaly detection defines as a problem of finding those data samples, which do not follow the patterns of the majority of data points. Among the variety of methods and algorithms proposed to deal with this problem, boundary based methods include One-class support vector machine (OC-SVM) is considered as an effective and outstanding one.Cited by: 12
https://ieeexplore.ieee.org/document/1223670/
Jul 24, 2003 · Time-series novelty detection, or anomaly detection, ... Time-series novelty detection using one-class support vector machines Abstract: Time-series novelty detection, or anomaly detection, refers to the automatic identification of novel or abnormal events embedded in normal time-series points. Although it is a challenging topic in data mining ...Cited by: 246
https://towardsdatascience.com/detecting-the-onset-of-machine-failure-using-anomaly-detection-techniques-d2f7a11eb809
Jul 19, 2019 · In the training phase, a classifier is learned using available labeled training data. Then the test instances are classified as normal or abnormal using the classifier trained in the initial step. The One-Class Support Vector Machine (OCSVM) and neural network methods are examples of such detection methods.Author: Animesh Goyal
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