A data reduction strategy and its application on scan and backscatter detection using rule-based classifiers

A data reduction strategy and its application on scan and backscatter detection using rule-based classifiers

Herrera Semenets, V. (Advanced Technologies Application Center)
Pérez-García, Osvaldo Andrés (Advanced Technologies Application Center)
Hernández-León, Raudel (Advanced Technologies Application Center)
van den Berg, Jan (TU Delft Information and Communication Technology; TU Delft Cyber Security)
Dörr, C. (TU Delft Cyber Security)

2018

In the last few years, the telecommunications scenario has experienced an increase in the volume of information generated, as well as in the execution of malicious activities. In order to complement Intrusion Detection Systems (IDSs), data mining techniques have begun to play a fundamental role in data analysis. On the other hand, the presence of useless information and the amount of data generated by telecommunication services (leading to a huge dimensional problem), can affect the performance of traditional IDSs. In this sense, a data preprocessing strategy is necessary to reduce data, but reducing data without affecting the accuracy of IDSs represents a challenge. In this paper, we propose a new data preprocessing strategy which reduces the number of features and instances in the training collection without greatly affecting the achieved accuracy of IDSs. Finally, our proposal is evaluated using four different rule-based classifiers, which are tested on real scan and backscatter data collected by a network telescope.

Backscatter
Data mining
Data reduction
Intrusion detection
Scan

http://resolver.tudelft.nl/uuid:0f4e3232-c291-4507-95ad-821c2908d8c3

https://doi.org/10.1016/j.eswa.2017.11.041

2022-01-03

0957-4174

Expert Systems with Applications, 95, 272-279

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2018 V. Herrera Semenets, Osvaldo Andrés Pérez-García, Raudel Hernández-León, Jan van den Berg, C. Dörr