TY - GEN
T1 - Detection and Prevention of a Malicious Activity in Industrial Federated Cloud Computing Paradigm
AU - Gerard, Akash
AU - Latif, Rabia
AU - Iqbal, Waseem
AU - Gerard, Naqash
AU - Husnain Johar, Ahmed
AU - Asghar, Umer
N1 - Publisher Copyright:
© 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - The concept of industrial federated cloud computing has reduced the computational cost of an individual user. On the other side, it has increased the security and privacy issues of the information of a user. Users sharing computational resources in a federated cloud can be malicious and can gain access to the sensitive data of users of other cloud providers. Therefore, there is a need to monitor the behavior of users, exchanging data between different cloud servers. Using intrusion detection techniques, we can avoid the malicious traffic from gaining an access of the critical data of the users of other cloud providers. Moreover, by adding intrusion prevention technique, we can make industrial cyber physical systems more robust and efficient. With the increase of usage of cloud computing in the industry, the latest technological trends are moving towards the automation. This paper covers the detection and prevention of malicious activities in an environment of industrial cloud federation.
AB - The concept of industrial federated cloud computing has reduced the computational cost of an individual user. On the other side, it has increased the security and privacy issues of the information of a user. Users sharing computational resources in a federated cloud can be malicious and can gain access to the sensitive data of users of other cloud providers. Therefore, there is a need to monitor the behavior of users, exchanging data between different cloud servers. Using intrusion detection techniques, we can avoid the malicious traffic from gaining an access of the critical data of the users of other cloud providers. Moreover, by adding intrusion prevention technique, we can make industrial cyber physical systems more robust and efficient. With the increase of usage of cloud computing in the industry, the latest technological trends are moving towards the automation. This paper covers the detection and prevention of malicious activities in an environment of industrial cloud federation.
KW - Artificial Intelligence
KW - Cloud computing
KW - Industrial cloud federation
KW - Intrusion detection
KW - Malicious traffic
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U2 - 10.1007/978-3-030-51041-1_52
DO - 10.1007/978-3-030-51041-1_52
M3 - Conference contribution
AN - SCOPUS:85088260309
SN - 9783030510404
T3 - Advances in Intelligent Systems and Computing
SP - 393
EP - 399
BT - Advances in Neuroergonomics and Cognitive Engineering - Proceedings of the AHFE 2020 Virtual Conferences on Neuroergonomics and Cognitive Engineering, and Industrial Cognitive Ergonomics and Engineering Psychology
A2 - Ayaz, Hasan
A2 - Asgher, Umer
PB - Springer
T2 - AHFE Virtual Conferences on Neuroergonomics and Cognitive Engineering, and Industrial Cognitive Ergonomics and Engineering Psychology, 2020
Y2 - 16 July 2020 through 20 July 2020
ER -