The Presentation of a Hybrid Anomaly Detection Model Using Inverse Weight Clustering and Machine Learning in Cloud Environments

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Jafar Gholi Beik A.; Shiri Ahmad Abadi M. E.; Rezakhani A.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: JOURNAL OF ELECTRONIC AND CYBER DEFENCE Year:2021 | Volume:9 | Issue:4 Page(s): 21-29

Download Full-Text

Persian Verion

View:

Download:

Cites:

Information Journal Paper

Title

The Presentation of a Hybrid Anomaly Detection Model Using Inverse Weight Clustering and Machine Learning in Cloud Environments

Author(s)

Jafar Gholi Beik A. | Shiri Ahmad Abadi M. E. | Rezakhani A. | Issue Writer Certificate

Keywords

IDS

Cloud Computing

Fog Computing

Anomaly Detection

IoT

Abstract

Today, due to highly advanced attacks and intrusions, it has become very difficult to detect IoT attacks in cloud environments. Other problems with cloud systems include low error detection accuracy, false positive rates, and long computation times. In the proposed method, we present a hybrid intrusion detection model including a clustering algorithm and a machine-based random forest classification for the fog and cloud environments. Also, to control the network traffic in the physical layer and also to detect the anomalies between IoT devices, calculations are performed on the fog and the edges of the cloud, so that after preprocessing, the incoming traffic to the fog and cloud are checked and if necessary, they are directed to an Anomaly Detection module. A random forest-based learning classification is used to identify the type of each attack. Both the general and cloud data have been used for this research. The overall accuracy, the mean false positive rate and the Anomaly Detection rate of the proposed intrusion detection system are 98. 03, 17% and 96. 30 respectively, which is notable in comparison to previous methods.

Multimedia

No record.

Cites

No record.

References

No record.

Cite

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans