Intrusion Detection System Using SVM as Classifier and GA for Optimizing Feature Vectors

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.

GHARAEE HOSSEIN; Fekri Maryam; Hosseinvand Hamid

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

Journal Paper

Paper Information

Journal: International Journal of Information and Communication Technology Research Year:2018 | Volume:10 | Issue:1 Page(s): 26-35

Download Full-Text

View:

195

Download:

174

Cites:

Information Journal Paper

Title

Intrusion Detection System Using SVM as Classifier and GA for Optimizing Feature Vectors

Author(s)

GHARAEE HOSSEIN | Fekri Maryam | Hosseinvand Hamid | Issue Writer Certificate

Keywords

Intrusion Detection System

Genetic

SVM

Feature Selection

Abstract

Nowadays, IDS is an essential technology for defense in depth. Researchers have interested on IDS using data mining and artificial intelligence (AI) techniques as an artful. IDSs can monitor system behavior and network traffic until detect intrusive action. One of the IDS models is anomaly based IDS which trained to distinguish between normal and abnormal traffic. This paper has proposed an anomaly based IDS using GA for optimizing feature vectors and SVM as a classifier. SVM has used as a supervised learning machine that analyses data and recognize patterns, used for classification and regression analysis. After optimization best features for SVM, IDS can detect abnormal traffic more accurate. There is an innovation in fitness function which is formed from TPR, FPR and the number of selected features. The new fitness function reduced the dimension of the data, increased true positive detection and simultaneously decreased false positive detection. In addition, the computation time for training will also have a remarkable reduction. This study proposes a method which can achieve more stable features in comparison with other techniques. The proposed model has been evaluated test with KDD CUP 99 and UNSW-NB15 datasets. Numeric Results and comparison to other models have been presented.

Multimedia

No record.

Cites

No record.

References

No record.

Cite

APA: Copy

GHARAEE, HOSSEIN, Fekri, Maryam, & Hosseinvand, Hamid. (2018). Intrusion Detection System Using SVM as Classifier and GA for Optimizing Feature Vectors. INTERNATIONAL JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGY RESEARCH, 10(1), 26-35. SID. https://sid.ir/paper/315157/en

Vancouver: Copy

GHARAEE HOSSEIN, Fekri Maryam, Hosseinvand Hamid. Intrusion Detection System Using SVM as Classifier and GA for Optimizing Feature Vectors. INTERNATIONAL JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGY RESEARCH[Internet]. 2018;10(1):26-35. Available from: https://sid.ir/paper/315157/en

IEEE: Copy

HOSSEIN GHARAEE, Maryam Fekri, and Hamid Hosseinvand, “Intrusion Detection System Using SVM as Classifier and GA for Optimizing Feature Vectors,” INTERNATIONAL JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGY RESEARCH, vol. 10, no. 1, pp. 26–35, 2018, [Online]. Available: https://sid.ir/paper/315157/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops