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Auflistung nach Autor:in "Laskov, Pavel"

1 - 4 von 4
  • Konferenzbeitrag
    Intrusion detection in unlabeled data with quarter-sphere support vector machines
    (Detection of intrusions and malware & vulnerability assessment, GI SIG SIDAR workshop, DIMVA 2004, 2004) Laskov, Pavel; Christin, Schäfer; Kotenko, Igor
    Practical application of data mining and machine learning techniques to intrusion detection is often hindered by the difficulty to produce clean data for the training. To address this problem a geometric framework for unsupervised anomaly detection has been recently proposed. In this framework, the data is mapped into a feature space, and anomalies are detected as the entries in sparsely populated regions. In this contribution we propose a novel formulation of a one-class Support Vector Machine (SVM) specially designed for typical IDS data features. The key idea of our "quarter-sphere" algorithm is to encompass the data with a hypersphere anchored at the center of mass of the data in feature space. The proposed method and its behavior on varying percentages of attacks in the data is evaluated on the KDDCup 1999 dataset.
  • Konferenzbeitrag
    Sicherer Umgang mit sensiblen Daten - technische Prävention und Reaktionen auf Datenschutzverletzungen
    (Informatik 2009 – Im Focus das Leben, 2009) Greveler, Ulrich; Laskov, Pavel; Pape, Sebastian
  • Konferenzbeitrag
    Sicherer Umgang mit sensiblen Daten – technische Prävention und Reaktionen auf Datenschutzverletzungen
    (Informatik 2009 – Im Focus das Leben, 2009) Greveler, Ulrich; Laskov, Pavel; Pape, Sebastian
  • Konferenzbeitrag
    Visualization of anomaly detection using prediction sensitivity
    (Sicherheit 2005, Sicherheit – Schutz und Zuverlässigkeit, 2005) Laskov, Pavel; Rieck, Konrad; Schäfer, Christin; Müller, Klaus-Robert
    Visualization of learning-based intrusion detection methods is a challenging problem. In this paper we propose a novel method for visualization of anomaly detection and feature selection, based on prediction sensitivity. The method allows an expert to discover informative features for separation of normal and attack instances. Experiments performed on the KDD Cup dataset show that explanations provided by prediction sensitivity reveal the nature of attacks. Application of prediction sensitivity for feature selection yields a major improvement of detection accuracy.