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An Active and Dynamic Botnet Detection Approach to Track Hidden Concept Drift

  • Zhi Wang
  • Meiqi Tian
  • Chunfu Jia
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10631)

Abstract

Nowadays, machine learning has been widely used as a core component in botnet detection systems. However, the assumption of machine learning algorithm is that the underlying botnet data distribution is stable for training and testing, which is vulnerable to well-crafted concept drift attacks, such as mimicry attacks, gradient descent attacks, poisoning attacks and so on. In this paper we present an active and dynamic learning approach to mitigate botnet hidden concept drift attacks. Instead of passively waiting for false negative, this approach could actively find the trend of hidden concept drift attacks using statistical p-values before performance starts to degenerate. And besides periodically retraining, this approach could dynamically reweight predictive features to track the trend of underlying concept drift. We test this approach on the public CTU botnet captures provided by malware capture facility project. The experiment results show that this approach could actively get insights of botnet hidden concept drift, and dynamically evolve to avoid model aging.

Keywords

Malware Botnet detection Concept drift Model aging horizontal correlation 

Notes

Acknowledgements

This material is based upon the work supported by the National Natural Science Foundation of China under the Grant No. 61300242 and No. 61772291, and by the Tianjin Research Program of Application Foundation and Advanced Technology under the Grant No. 15JCQNJC41500 and No. 17JCZDJC30500, and by the Open Project Foundation of Information Security Evaluation Center of Civil Aviation, Civil Aviation University of China under the Grant No. CAAC-ISECCA-201701 and No. CAAC-ISECCA-201702, and by the National Key Basic Research Program of China under the Grant No. 2013CB834204.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Computer and Control EngineeringNankai UniversityTianjinChina

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