Robust Anomaly Detection Using Support Vector Machines

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Robust Anomaly Detection Using Support Vector Machines

    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

Robust Anomaly Detection Using Support Vector Machines

    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 ...

Robust Support Vector Machines for Anomaly Detection in ...

    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

Robust Anomaly Detection Using Support Vector Machines

    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.

(PDF) Robust Support Vector Machines for Anomaly Detection ...

    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 ...

Anomaly Detection Using Support Vector Machines SpringerLink

    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

Ramp loss one-class support vector machine; A robust and ...

    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

Time-series novelty detection using one-class support ...

    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

Detecting the Onset of Machine Failure using Anomaly ...

    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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