Intrusion Detection with Low False Alarms using Decision Tree-based SVM Classifier

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.

Tajari Siahmarzkooh Aliakbar

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

Journal Paper

Paper Information

Journal: International Journal of Web Research Year:2020 | Volume:3 | Issue:2 Page(s): 45-50

Download Full-Text

View:

Download:

Cites:

Information Journal Paper

Title

Intrusion Detection with Low False Alarms using Decision Tree-based SVM Classifier

Author(s)

Tajari Siahmarzkooh Aliakbar | Issue Writer Certificate

Keywords

Intrusion Detection

K-Means Clustering

Decision Tree

Support Vector Classifier

NSL-KDD Dataset

Abstract

Todays, Intrusion Detection Systems (IDS) are considered as key components of security networks. However, high false positive and false negative rates are the important problems of these systems. On the other hand, many of the existing solutions in the articles are restricted to class datasets due to the use of a specific technique, but in real applications they may have multi-variant datasets. With the impetus of the facts, this paper presents a new anomaly based Intrusion Detection system using J48 Decision Tree, Support Vector Classifier (SVC) and K-Means Clustering algorithm in order to reduce false alarm rates and enhance the system performance. J48 Decision Tree algorithm is used to select the best features and optimize the dataset. Also, an SVM classifier and a modified K-Means Clustering algorithm are used to build a profile of normal and anomalous behaviors of dataset. Simulation results on benchmark NSL-KDD, CICIDS2017 and synthetic datasets confirm that the proposed method has significant performance in comparison with previous approaches.

Multimedia

No record.

Cites

No record.

References

No record.

Cite

APA: Copy

Tajari Siahmarzkooh, Aliakbar. (2020). Intrusion Detection with Low False Alarms using Decision Tree-based SVM Classifier. INTERNATIONAL JOURNAL OF WEB RESEARCH, 3(2), 45-50. SID. https://sid.ir/paper/1044239/en

Vancouver: Copy

Tajari Siahmarzkooh Aliakbar. Intrusion Detection with Low False Alarms using Decision Tree-based SVM Classifier. INTERNATIONAL JOURNAL OF WEB RESEARCH[Internet]. 2020;3(2):45-50. Available from: https://sid.ir/paper/1044239/en

IEEE: Copy

Aliakbar Tajari Siahmarzkooh, “Intrusion Detection with Low False Alarms using Decision Tree-based SVM Classifier,” INTERNATIONAL JOURNAL OF WEB RESEARCH, vol. 3, no. 2, pp. 45–50, 2020, [Online]. Available: https://sid.ir/paper/1044239/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops