[1]JIANG Hongling,SHAO Xiuli.Botnet detection algorithm based on neural network[J].CAAI Transactions on Intelligent Systems,2013,8(2):113-118.[doi:10.3969/j.issn.1673-4785.201210055]
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Botnet detection algorithm based on neural network

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 8 Number of periods: 2013 2 Page number: 113-118 Column: 学术论文—机器学习 Public date: 2013-04-25
Title:
Botnet detection algorithm based on neural network
Author(s):
JIANG Hongling;  SHAO Xiuli
College of Information Technical Science, Nankai University, Tianjin 300071, China
Keywords:
botnet;  BP neural network;  feature vector;  network traffic;  detection algorithm
CLC:
TP393
DOI:
10.3969/j.issn.1673-4785.201210055
Abstract:
The most current botnet detection algorithm are typically based on network traffic analyzing technologies that usually need packet payload. The botnet detection algorithm also relies on information obtained by external systems or malicious behaviors of bots that do not automatically store the features of botnet traffic and do not have the ability of associative memory. As a result, this paper proposes a botnet detection algorithm based on BP neural network which trains the BP neural network classifier through a lot of botnet and normal traffic samples and allows it to learn how to identify botnet traffic and automatically remember the features of botnet traffic and therefore, detect the infected hosts effectively. The neural network classifier takes the host-pairs as analysis objects, extracts the traffic features of communications between two hosts and takes the feature vectors of host-pairs as input, thus, effectively distinguishing the normal hosts and bots. The experimental results show that the detection rate of our algorithm can achieve to 99% and the false positive rate is below 1% and the algorithm has a good performance.
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