Attack Detection in Cloud Infrastructures Using Artificial Neural Network with Genetic Feature Selection
Sayantan Guha, Stephen S. Yau, Arun Balaji Buduru · 2016
Detecting cyber-attacks in cloud infrastructures is essential for protecting cloud infrastructures from cyber-attacks. It is difficult to detect cyber-attacks in cloud infrastructures due to the complex and distributed natures of cloud infrastructures. In addition, various computing and storage devices, both mobile and stationary, are connected to cloud infrastructures to facilitate users access, which increases the difficulty and complexity of cyber-attack detection. In this paper, an effective approach is presented to detecting cyber-attacks in cloud infrastructures, including those through remote computing devices. This approach is to use an artificial neural network (ANN), which is trained using the network traffic data on the connecting links of the cloud infrastructures. Since ANN is computationally intensive, a technique using a genetic algorithm to reduce the number of features extracted from the network traffic data is developed and incorporated in our approach. This approach is illustrated by using two large data sets of network traffic, and shown that the results are better than those of existing methods for detecting cyber-attacks in cloud infrastructures.