Skip to main content
main-content
Top

Hint

Swipe to navigate through the articles of this issue

02-11-2020 | Original Article

usfAD: a robust anomaly detector based on unsupervised stochastic forest

Journal:
International Journal of Machine Learning and Cybernetics
Authors:
Sunil Aryal, K.C. Santosh, Richard Dazeley

Abstract

In real-world applications, data can be represented using different units/scales. For example, weight in kilograms or pounds and fuel-efficiency in km/l or l/100 km. One unit can be a linear or non-linear scaling of another. The variation in metrics due to the non-linear scaling makes Anomaly Detection (AD) challenging. Most existing AD algorithms rely on distance- or density-based functions, which makes them sensitive to how data is expressed. This means that they are representation dependent. To avoid such a problem, we introduce a new anomaly detection method, which we call ‘usfAD: Unsupervised Stochastic Forest-based Anomaly Detector’. Our empirical evaluation in synthetic and real-world cybersecurity (spam detection, malicious URL detection and intrusion detection) datasets shows that our approach is more robust to the variation in units/scales used to express data. It produces more consistent and better results than five state-of-the-art AD methods namely: local outlier factor; one-class support vector machine; isolation forest; nearest neighbor in a random subsample of data; and, simple histogram-based probabilistic method.

Please log in to get access to this content

To get access to this content you need the following product:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 69.000 Bücher
  • über 500 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Umwelt
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Testen Sie jetzt 30 Tage kostenlos.

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 50.000 Bücher
  • über 380 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Umwelt
  • Maschinenbau + Werkstoffe




Testen Sie jetzt 30 Tage kostenlos.

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 58.000 Bücher
  • über 300 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Testen Sie jetzt 30 Tage kostenlos.

Literature
About this article