TY - GEN
T1 - A Framework for Data Security in Cloud using Collaborative Intrusion Detection Scheme
AU - Nagar, Upasana
AU - He, Xiangjian
AU - Nanda, Priyadarsi
AU - Tan, Zhiyuan
N1 - Publisher Copyright:
© 2017 Association for Computing Machinery.
PY - 2017/10/13
Y1 - 2017/10/13
N2 - Cloud computing offers an on demand, elastic, global network access to a shared pool of resources that can be configured on user demand. The advantages of cloud computing are lucrative for well-established organizations looking to reduce infrastructure cost overheads. However, the users are not quite confident in entrusting their data to the cloud due to security threats and risks perceived in the cloud domain. Issues involving privacy requirements for the cloud and best practices in the cloud are suggested in this paper. Although the cloud provider ensures security in the cloud yet the flow of data, storage location, data computing process and security breaches are not transparent to the cloud customer. This distrust and lack of control on data is a major hindrance for potential cloud customers in adopting the cloud models for their businesses. Intrusion Detection Systems (IDSs) are widely used to detect malicious activities. However existing solutions with IDSs involving DDoS and other non-detectable events may not be suitable in applying to the cloud due to distributed data storage and a major shift in Internet access mechanisms offered by cloud providers. Hence there is a strong need to analyze an appropriate IDS to counter DDoS attacks in the cloud. In this paper we propose a novel framework for data security in the cloud using Collaborative Intrusion Detection (CIDS) scheme. The benefits of CIDS scheme in cloud are enabling the end user to get comprehensive information in the event of a distributed attack on cloud.
AB - Cloud computing offers an on demand, elastic, global network access to a shared pool of resources that can be configured on user demand. The advantages of cloud computing are lucrative for well-established organizations looking to reduce infrastructure cost overheads. However, the users are not quite confident in entrusting their data to the cloud due to security threats and risks perceived in the cloud domain. Issues involving privacy requirements for the cloud and best practices in the cloud are suggested in this paper. Although the cloud provider ensures security in the cloud yet the flow of data, storage location, data computing process and security breaches are not transparent to the cloud customer. This distrust and lack of control on data is a major hindrance for potential cloud customers in adopting the cloud models for their businesses. Intrusion Detection Systems (IDSs) are widely used to detect malicious activities. However existing solutions with IDSs involving DDoS and other non-detectable events may not be suitable in applying to the cloud due to distributed data storage and a major shift in Internet access mechanisms offered by cloud providers. Hence there is a strong need to analyze an appropriate IDS to counter DDoS attacks in the cloud. In this paper we propose a novel framework for data security in the cloud using Collaborative Intrusion Detection (CIDS) scheme. The benefits of CIDS scheme in cloud are enabling the end user to get comprehensive information in the event of a distributed attack on cloud.
KW - Alert Correlation
KW - Cloud Security
KW - Collaborative Intrusion Detection
UR - http://www.scopus.com/inward/record.url?scp=85042133469&partnerID=8YFLogxK
U2 - 10.1145/3136825.3136905
DO - 10.1145/3136825.3136905
M3 - Conference contribution
AN - SCOPUS:85042133469
T3 - ACM International Conference Proceeding Series
SP - 188
EP - 193
BT - Security of Information and Networks - 10th International Conference, SIN 2017
A2 - Dhaka, Vijaypal Singh
A2 - Elci, Atilla
A2 - Gaur, Manoj Singh
A2 - Bohra, Manoj Kumar
A2 - Shekhawat, Rajveer Singh
PB - Association for Computing Machinery
T2 - 10th International Conference on Security of Information and Networks, SIN 2017
Y2 - 13 October 2017 through 15 October 2017
ER -