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Spam transaction attack detection model based on GRU and WGAN-div
Computer Communications ( IF 2.816 ) Pub Date : 2020-07-27 , DOI: 10.1016/j.comcom.2020.07.031
Jin Yang; Tao Li; Gang Liang; YunPeng Wang; TianYu Gao; FangDong Zhu

A Spam Transaction attack is a kind of hostile attack activity specifically targeted against a Cryptocurrency Network. Traditional network intrusion detection methods lack the capability of automatic feature extraction for spam transaction attacks, and thus the detection efficiency is low. Worse still, these kinds of attack methods and the key intrusion behaviour process are usually concealed and submerged into a large number of normal data packages; therefore, the captured threat test samples are too small, which easily leads to insufficient training of detection model, low detection accuracy rate, and high false alarm rate. In this paper, a spam transaction intrusion detection model based on GRU(Gated Recurrent Unit) is proposed, which takes advantage of the excellent features of deep learning and uses repeated and multilevel learning to perform automatic feature extraction for network intrusion behaviour. The model has extremely high learning ability and massive data processing ability. Moreover, it has a quicker and more accurate spam transaction attack detection ability than traditional intrusion detection algorithms. Additionally, a generation method of spam transaction-samples based on WGAN-div is proposed, which obtains new samples by learning training samples and solves the problems of insufficient original samples and unbalanced samples. A series of experiments were performed to verify the proposed models. The proposed models can distinguish between normal and abnormal transaction behaviours with an accuracy reaching to 99.86%. The experimental results indicate that the proposed models in this paper have higher efficiency and accuracy in detecting spam transaction attacks, which provides a novel and better idea for research of spam transaction attack detection systems.

更新日期:2020-08-01

 

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