Attack Detection in Cloud Infrastructures Using Artificial Neural Network with Genetic Feature Selection

Sayantan Guha, Sik-Sang Yau, Arun Balaji Buduru

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings - 2016 IEEE 14th International Conference on Dependable, Autonomic and Secure Computing, DASC 2016, 2016 IEEE 14th International Conference on Pervasive Intelligence and Computing, PICom 2016, 2016 IEEE 2nd International Conference on Big Data Intelligence and Computing, DataCom 2016 and 2016 IEEE Cyber Science and Technology Congress, CyberSciTech 2016, DASC-PICom-DataCom-CyberSciTech 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages414-419
Number of pages6
ISBN (Electronic)9781509040650
DOIs
StatePublished - Oct 11 2016
Event14th IEEE International Conference on Dependable, Autonomic and Secure Computing, DASC 2016, 14th IEEE International Conference on Pervasive Intelligence and Computing, PICom 2016, 2nd IEEE International Conference on Big Data Intelligence and Computing, DataCom 2016 and 2016 IEEE Cyber Science and Technology Congress, CyberSciTech 2016, DASC-PICom-DataCom-CyberSciTech 2016 - Auckland, New Zealand
Duration: Aug 8 2016Aug 10 2016

Other

Other14th IEEE International Conference on Dependable, Autonomic and Secure Computing, DASC 2016, 14th IEEE International Conference on Pervasive Intelligence and Computing, PICom 2016, 2nd IEEE International Conference on Big Data Intelligence and Computing, DataCom 2016 and 2016 IEEE Cyber Science and Technology Congress, CyberSciTech 2016, DASC-PICom-DataCom-CyberSciTech 2016
CountryNew Zealand
CityAuckland
Period8/8/168/10/16

Keywords

  • artificial neural network
  • Cloud infrastructures
  • cyber-attack detection
  • feature selection
  • genetic algorithm

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Information Systems
  • Computer Science (miscellaneous)
  • Artificial Intelligence
  • Computer Networks and Communications

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    Guha, S., Yau, S-S., & Buduru, A. B. (2016). Attack Detection in Cloud Infrastructures Using Artificial Neural Network with Genetic Feature Selection. In Proceedings - 2016 IEEE 14th International Conference on Dependable, Autonomic and Secure Computing, DASC 2016, 2016 IEEE 14th International Conference on Pervasive Intelligence and Computing, PICom 2016, 2016 IEEE 2nd International Conference on Big Data Intelligence and Computing, DataCom 2016 and 2016 IEEE Cyber Science and Technology Congress, CyberSciTech 2016, DASC-PICom-DataCom-CyberSciTech 2016 (pp. 414-419). [7588878] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DASC-PICom-DataCom-CyberSciTec.2016.32