LEARNING A NEW DISTANCE METRIC TO IMPROVE AN SVM-CLUSTERING BASED INTRUSION DETECTION SYSTEM

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.

Aliabkabri sani Roya; GHASEMI ABDORASOUL

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

Seminar Paper

Paper Information

Seminar: INTERNATIONAL SYMPOSIUM ON ARTIFICIAL INTELLIGENCE AND SIGNAL PROCESSING (AISP)
Year:2015 | Issue:1

Download Full-Text

View:

129

Download:

103

Information Seminar Paper

Title

LEARNING A NEW DISTANCE METRIC TO IMPROVE AN SVM-CLUSTERING BASED INTRUSION DETECTION SYSTEM

Author(s)

Aliabkabri sani Roya | GHASEMI ABDORASOUL | Issue Writer Certificate

Keywords

METRIC LEARNING

INTRUSION DETECTION SYSTEM

ANOMALY DETECTION

CLUSTERING ALGORITHMS

Abstract

&NBSP;
&NBSP;IN THE RECENT DECADES, MANY INTRUSION DETECTION SYSTEMS (IDSS) HAVE BEEN PROPOSED TO ENHANCE THE SECURITY OF NETWORKS. A CLASS OF IDSS IS BASED ON CLUSTERING OF NETWORK TRAFFIC INTO NORMAL AND ABNORMAL ACCORDING TO SOME FEATURES OF THE CONNECTIONS. THE SELECTED DISTANCE FUNCTION TO MEASURE THE SIMILARITY AND DISSIMILARITY OF SESSIONS' FEATURES AFFECT THE PERFORMANCE OF CLUSTERING BASED IDSS. THE MOST POPULAR DISTANCE METRIC, WHICH IS USED IN DESIGNING THESE IDSS IS THE EUCLIDEAN DISTANCE FUNCTION. IN THIS PAPER, WE ARGUE THAT MORE APPROPRIATE DISTANCE FUNCTIONS CAN BE DEPLOYED FOR IDSS. WE PROPOSE A METHOD OF LEARNING AN APPROPRIATE DISTANCE FUNCTION ACCORDING TO A SET OF SUPERVISION INFORMATION. THIS METRIC IS DERIVED BY SOLVING A SEMI-DEFINITE OPTIMIZATION PROBLEM, WHICH ATTEMPTS TO DECREASE THE DISTANCE BETWEEN THE SIMILAR, AND INCREASES THE DISTANCES BETWEEN THE DISSIMILAR FEATURE VECTORS. THE EVALUATION OF THIS SCHEME OVER KYOTO2006+ DATASET SHOWS THAT THE NEW DISTANCE METRIC, CAN IMPROVE THE PERFORMANCE OF A SUPPORT VECTOR MACHINE (SVM) CLUSTERING BASED IDS IN TERMS OF NORMAL DETECTION AND FALSE POSITIVE RATES. ...

Multimedia

No record.

Cites

No record.

References

No record.

Cite

APA: Copy

Aliabkabri sani, Roya, & GHASEMI, ABDORASOUL. (2015). LEARNING A NEW DISTANCE METRIC TO IMPROVE AN SVM-CLUSTERING BASED INTRUSION DETECTION SYSTEM. INTERNATIONAL SYMPOSIUM ON ARTIFICIAL INTELLIGENCE AND SIGNAL PROCESSING (AISP). SID. https://sid.ir/paper/927455/en

Vancouver: Copy

Aliabkabri sani Roya, GHASEMI ABDORASOUL. LEARNING A NEW DISTANCE METRIC TO IMPROVE AN SVM-CLUSTERING BASED INTRUSION DETECTION SYSTEM. 2015. Available from: https://sid.ir/paper/927455/en

IEEE: Copy

Roya Aliabkabri sani, and ABDORASOUL GHASEMI, “LEARNING A NEW DISTANCE METRIC TO IMPROVE AN SVM-CLUSTERING BASED INTRUSION DETECTION SYSTEM,” presented at the INTERNATIONAL SYMPOSIUM ON ARTIFICIAL INTELLIGENCE AND SIGNAL PROCESSING (AISP). 2015, [Online]. Available: https://sid.ir/paper/927455/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops