Retracted: Efficient ensemble to combat flash attacks

Abstract

AbstractFlash event generates enormous traffic and the cloud service providers use sustaining techniques like scaling and content delivery network to up their services. One of the main bottlenecks that the cloud service providers still find difficult to tackle is flash attacks. Illegitimate users send craftily designed packets to land up inside the server for wreaking havoc. As deep learning autoencoder has the potential to detect malicious traffic it has been used in this research study to develop an ensemble. Convolutional neural network is efficacious in overcoming the issue of overfitting; deep autoencoder is proficient in extracting features through dimensionality reduction. In order to obtain both these advantages it was decided to develop an ensemble keeping denoising autoencoder as the core element. The process of addressing a flash attack requires first detecting the presence of bot in malicious traffic, second studying its nature by observing its behavioral manifestations. Detection of botnet was achieved by three ensembles, namely, DAE_CNN, DAE_MLP, and DAE_XGB. But capturing its external manifested behavior is challenging, because the bot signatures are always in a state of flux. The simulated empirical study yielded an appreciable outcome. Its accuracy rate was 99.9% for all the three models and the false positive rates were 0, 0.006, and 0.001, respectively.