標題: Effective Botnet Detection Through Neural Networks on Convolutional Features
作者: Chen, Shao-Chien
Chen, Yi-Ruei
Tzeng, Wen-Guey
資訊工程學系
Department of Computer Science
關鍵字: Botnet detection;machine learning;convolutional neural networks
公開日期: 1-一月-2018
摘要: Botnet is one of the major threats on the Internet for committing cybercrimes, such as DDoS attacks, stealing sensitive information, spreading spams, etc. It is a challenging issue to detect modern botnets that are continuously improving for evading detection. In this paper, we propose a machine learning based botnet detection system that is shown to be effective in identifying P2P botnets. Our approach extracts convolutional version of effective flow-based features, and trains a classification model by using a feed-forward artificial neural network. The experimental results show that the accuracy of detection using the convolutional features is better than the ones using the traditional features. It can achieve 94.7% of detection accuracy and 2.2% of false positive rate on the known P2P botnet datasets. Furthermore, our system provides an additional confidence testing for enhancing performance of botnet detection. It further classifies the network traffic of insufficient confidence in the neural network. The experiment shows that this stage can increase the detection accuracy up to 98.6% and decrease the false positive rate up to 0.5%.
URI: http://dx.doi.org/10.1109/TrustCom/BigDataSE.2018.00062
http://hdl.handle.net/11536/153340
ISBN: 978-1-5386-4387-7
ISSN: 2324-9013
DOI: 10.1109/TrustCom/BigDataSE.2018.00062
期刊: 2018 17TH IEEE INTERNATIONAL CONFERENCE ON TRUST, SECURITY AND PRIVACY IN COMPUTING AND COMMUNICATIONS (IEEE TRUSTCOM) / 12TH IEEE INTERNATIONAL CONFERENCE ON BIG DATA SCIENCE AND ENGINEERING (IEEE BIGDATASE)
起始頁: 372
結束頁: 378
顯示於類別:會議論文