Presenting a Malware Detection System by Implementing Hardware Counters Based on the Multi-Layer Perceptron Neural Network (MLP) and the Dragonfly Optimization Algorithm

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.

Karami M.; Mosleh M.

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:2 Page(s): 9-16

Download Full-Text

Persian Verion

View:

Download:

Cites:

Information Journal Paper

Title

Presenting a Malware Detection System by Implementing Hardware Counters Based on the Multi-Layer Perceptron Neural Network (MLP) and the Dragonfly Optimization Algorithm

Author(s)

Karami M. | Mosleh M. | Issue Writer Certificate

Keywords

MalwareQ4

Hardware CountersQ4

Multi-Layer Perceptron Neural NetworkQ4

Dragonfly Optimization AlgorithmQ4

Abstract

Today, one of the most important challenges of information security and communication networks is the increasing number of Malware and, consequently, finding suitable ways to protect systems against them. Knowing in time and finding ways to deal with the malicious effects of Malware is one of the most important challenges for programmers and information security professionals. Is. Intelligent Malware detection systems are able to model malicious behavior well. Extracting appropriate features and using efficient classifiers can improve the performance of such systems. In this paper, a new approach to Malware detection is proposed using synergy of the features of the Hardware Counters and the optimization of the multilayer perceptron neural network classifier. The proposed system is able to identify healthy files from Malware by extracting features with high discrimination and also using the neural network optimized by the dragonfly algorithm. In order to evaluate the proposed system, a data set including 168 healthy samples and 437 samples infected with Malware is used. The results of the simulations show the higher performance of the proposed category compared to other categories, so that the proposed system has been able to detect the presence of Malware-infected files with 86% accuracy.

Cites

No record.

References

No record.

Cite

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops