A P2P Botnet detection scheme based on decision tree and adaptive multilayer neural networks

Neural Computing & Applications
Mohammad AlauthamanM A Hossain

Abstract

In recent years, Botnets have been adopted as a popular method to carry and spread many malicious codes on the Internet. These malicious codes pave the way to execute many fraudulent activities including spam mail, distributed denial-of-service attacks and click fraud. While many Botnets are set up using centralized communication architecture, the peer-to-peer (P2P) Botnets can adopt a decentralized architecture using an overlay network for exchanging command and control data making their detection even more difficult. This work presents a method of P2P Bot detection based on an adaptive multilayer feed-forward neural network in cooperation with decision trees. A classification and regression tree is applied as a feature selection technique to select relevant features. With these features, a multilayer feed-forward neural network training model is created using a resilient back-propagation learning algorithm. A comparison of feature set selection based on the decision tree, principal component analysis and the ReliefF algorithm indicated that the neural network model with features selection based on decision tree has a better identification accuracy along with lower rates of false positives. The usefulness of the proposed appro...Continue Reading

Citations

Jul 30, 2020··Guilherme Henrique RibeiroElaine R. de Faria Paiva

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Methods Mentioned

BETA
features extraction
PCA

Software Mentioned

Windows
Skype
TAMD
CART
MATLAB
eMule
BotSniffer
BitTorrent
WEKA
PCAP

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