Smart Detection of Covert Worms in Computer Networks

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.

ZABIHI M.; MINAEI B.; NASIRI 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:2020 | Volume:7 | Issue:4 Page(s): 67-77

Download Full-Text

Persian Verion

View:

410

Download:

Cites:

Information Journal Paper

Title

Smart Detection of Covert Worms in Computer Networks

Author(s)

ZABIHI M. | MINAEI B. | NASIRI M. | Issue Writer Certificate

Keywords

WormQ1

DetectorQ1

Zero dayQ1

ScanQ1

Data miningQ1

Abstract

Complex malwares which infiltrate systems in a country’ s critical infrastructure with the purpose of destruction or espionage are major threats in cyber space. What is presented in this article is a smart solution to discover Zero day Worms which can be polymorphic and encrypted and their nature is still unknown to defense tools. To do this, we first outlined our desirable Detector and then presented a solution based on Data mining methods for detecting malicious extensions with the emphasis on Worm’ s Scanning feature, communication model of the infected hosts and the packets’ headers transmitted across the network. By clustering clean data, and using clean and contaminated data classifications, experimental samples and the C5 decision tree, we managed to present the best model with an accuracy of 94. 49%, precision of 92. 92%, and a recall of 94. 70% in identifying infected packages from the clean ones. Finally, we also showed that the use of clustering in the patterns of clean hosts’ traffic could reach better results in identifying infected traffic.

Cites

No record.

References

No record.

Cite

APA: Copy

ZABIHI, M., MINAEI, B., & NASIRI, M.. (2020). Smart Detection of Covert Worms in Computer Networks. JOURNAL OF ELECTRONIC AND CYBER DEFENCE, 7(4 ), 67-77. SID. https://sid.ir/paper/359580/en

Vancouver: Copy

ZABIHI M., MINAEI B., NASIRI M.. Smart Detection of Covert Worms in Computer Networks. JOURNAL OF ELECTRONIC AND CYBER DEFENCE[Internet]. 2020;7(4 ):67-77. Available from: https://sid.ir/paper/359580/en

IEEE: Copy

M. ZABIHI, B. MINAEI, and M. NASIRI, “Smart Detection of Covert Worms in Computer Networks,” JOURNAL OF ELECTRONIC AND CYBER DEFENCE, vol. 7, no. 4 , pp. 67–77, 2020, [Online]. Available: https://sid.ir/paper/359580/en

Related Journal Papers