TY - GEN
T1 - Attack Detection in Cloud Infrastructures Using Artificial Neural Network with Genetic Feature Selection
AU - Guha, Sayantan
AU - Yau, Sik-Sang
AU - Buduru, Arun Balaji
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/10/11
Y1 - 2016/10/11
N2 - 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.
AB - 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.
KW - Cloud infrastructures
KW - artificial neural network
KW - cyber-attack detection
KW - feature selection
KW - genetic algorithm
UR - http://www.scopus.com/inward/record.url?scp=84995393630&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84995393630&partnerID=8YFLogxK
U2 - 10.1109/DASC-PICom-DataCom-CyberSciTec.2016.32
DO - 10.1109/DASC-PICom-DataCom-CyberSciTec.2016.32
M3 - Conference contribution
AN - SCOPUS:84995393630
T3 - 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
SP - 414
EP - 419
BT - 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
A2 - Wang, Kevin I-Kai
A2 - Jin, Qun
A2 - Bhuiyan, Md Zakirul Alam
A2 - Zhang, Qingchen
A2 - Hsu, Ching-Hsien
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th 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
Y2 - 8 August 2016 through 10 August 2016
ER -