Title
Author
Year
Volume
:
2024
Vol. 1
no. 1
Vol. 2
2023
Vol. 1
no. 1
no. 2
2022
Vol. 1
no. 1
2021
Vol. 1
no. 1
2019
Vol. 1
no. 1
no. 2
2013
Vol. 1
no. 1
Vol. 2
no. 1
2011
Vol. 1
no. 1
Vol. 2
no. 1
2010
Vol. 1
no. 1
no. 2
Vol. 2
Vol. 0
Vol. 1
Distributed Intrusion Prevention System Based on Cloud Computing in the Internet of Things
Pages
:
20-26
Mohaddese Mir, and Hamid Reza Naji
Cyber security is a serious issue in cyberspace. Thousands of zero-day attacks are constantlyevolving due to the addition of various protocols on the Internet of Things. On the other hand,using deep learning in various information security fields has been successful. Also using deeplearning to detect an attack in cyberspace is a flexible mechanism for detecting new attacks. Inthis research, we introduced a distributed penetration system for the Internet of Things, whichused Apache Spark and also an expert system for optimal performance. The proposed model iscompared with the suggested architecture of a distributed attack detection scheme using the deeplearning approach for the Internet of Things. Also, the effectiveness of the proposed deep learningagainst the shallow learning algorithms for detecting attacks on the Internet of Things isevaluated. The results indicated that the proposed approach is more optimal in accuracy, learningspeed and memory consumption in compare to another discussed method.
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