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

Sayantan Guha; Sik-Sang Yau; Arun Balaji Buduru

doi:10.1109/DASC-PICom-DataCom-CyberSciTec.2016.32

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 proceeding › Conference 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 language	English (US)
Title of host publication	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
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	414-419
Number of pages	6
ISBN (Electronic)	9781509040650
DOIs	https://doi.org/10.1109/DASC-PICom-DataCom-CyberSciTec.2016.32
State	Published - Oct 11 2016
Event	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 - Auckland, New Zealand Duration: Aug 8 2016 → Aug 10 2016

Other

Other	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
Country	New Zealand
City	Auckland
Period	8/8/16 → 8/10/16

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

Access to Document

10.1109/DASC-PICom-DataCom-CyberSciTec.2016.32

Fingerprint Dive into the research topics of 'Attack Detection in Cloud Infrastructures Using Artificial Neural Network with Genetic Feature Selection'. Together they form a unique fingerprint.

Cite this

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

Attack Detection in Cloud Infrastructures Using Artificial Neural Network with Genetic Feature Selection. / Guha, Sayantan; Yau, Sik-Sang; Buduru, Arun Balaji.

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. Institute of Electrical and Electronics Engineers Inc., 2016. p. 414-419 7588878.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Guha, S, Yau, S-S & Buduru, AB 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., 7588878, Institute of Electrical and Electronics Engineers Inc., pp. 414-419, 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, Auckland, New Zealand, 8/8/16. https://doi.org/10.1109/DASC-PICom-DataCom-CyberSciTec.2016.32

Guha S, Yau S-S, Buduru AB. 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. Institute of Electrical and Electronics Engineers Inc. 2016. p. 414-419. 7588878 https://doi.org/10.1109/DASC-PICom-DataCom-CyberSciTec.2016.32

Guha, Sayantan ; Yau, Sik-Sang ; Buduru, Arun Balaji. / Attack Detection in Cloud Infrastructures Using Artificial Neural Network with Genetic Feature Selection. 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. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 414-419

@inproceedings{ab08b092ce8a44bcb5ba80cd688e538a,

title = "Attack Detection in Cloud Infrastructures Using Artificial Neural Network with Genetic Feature Selection",

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.",

keywords = "artificial neural network, Cloud infrastructures, cyber-attack detection, feature selection, genetic algorithm",

author = "Sayantan Guha and Sik-Sang Yau and Buduru, {Arun Balaji}",

year = "2016",

month = oct,

day = "11",

doi = "10.1109/DASC-PICom-DataCom-CyberSciTec.2016.32",

language = "English (US)",

pages = "414--419",

booktitle = "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",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "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 ; Conference date: 08-08-2016 Through 10-08-2016",

}

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

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 - artificial neural network

KW - Cloud infrastructures

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

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

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 -