Principle component analysis based intrusion detection system using support vector machine

N. S.K.H. Praneeth; Naveen M. Varma; Roshan Ramakrishna Naik

doi:10.1109/RTEICT.2016.7808050

Principle component analysis based intrusion detection system using support vector machine

N. S.K.H. Praneeth, Naveen M. Varma, Roshan Ramakrishna Naik

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

33 Citations (Scopus)

Abstract

Whenever an intrusion happens, privacy and security of the system are compromised. In order to detect different types of attacks that happen in a network, Intrusion Detection System (IDS) plays a crucial role in Network security. IDS is designed in order to classify the activities of the system into abnormal and normal. Machine learning based Intrusion Detection is gaining attention in recent years and is able to give better results with greater accuracy and high detection rate on novel attacks. In this paper, performance of different kernels of Support Vector Machine (SVM) are evaluated against Knowledge Discovery in Databases Cup'99(KDD) data set and detection accuracy, detection time are compared. The detection time is reduced by adopting Principal Component Analysis (PCA) which curtails higher dimensional dataset to lower dimensional dataset. The experiments which are conducted in this research shows that Gaussian Radial Basis Function kernel of SVM has higher detection accuracy.

Original language	English
Title of host publication	2016 IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2016 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1344-1350
Number of pages	7
ISBN (Electronic)	9781509007745
DOIs	https://doi.org/10.1109/RTEICT.2016.7808050
Publication status	Published - 05-01-2017
Externally published	Yes
Event	1st IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2016 - Bangalore, India Duration: 20-05-2016 → 21-05-2016

Conference

Conference	1st IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2016
Country/Territory	India
City	Bangalore
Period	20-05-16 → 21-05-16

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Computer Science Applications
Information Systems
Electrical and Electronic Engineering

Access to Document

10.1109/RTEICT.2016.7808050

Cite this

Praneeth, N. S. K. H., Varma, N. M., & Naik, R. R. (2017). Principle component analysis based intrusion detection system using support vector machine. In 2016 IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2016 - Proceedings (pp. 1344-1350). [7808050] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/RTEICT.2016.7808050

Praneeth, N. S.K.H. ; Varma, Naveen M. ; Naik, Roshan Ramakrishna. / Principle component analysis based intrusion detection system using support vector machine. 2016 IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1344-1350

@inproceedings{8831cd466f5247ab92d0ecef05c9c186,

title = "Principle component analysis based intrusion detection system using support vector machine",

abstract = "Whenever an intrusion happens, privacy and security of the system are compromised. In order to detect different types of attacks that happen in a network, Intrusion Detection System (IDS) plays a crucial role in Network security. IDS is designed in order to classify the activities of the system into abnormal and normal. Machine learning based Intrusion Detection is gaining attention in recent years and is able to give better results with greater accuracy and high detection rate on novel attacks. In this paper, performance of different kernels of Support Vector Machine (SVM) are evaluated against Knowledge Discovery in Databases Cup'99(KDD) data set and detection accuracy, detection time are compared. The detection time is reduced by adopting Principal Component Analysis (PCA) which curtails higher dimensional dataset to lower dimensional dataset. The experiments which are conducted in this research shows that Gaussian Radial Basis Function kernel of SVM has higher detection accuracy.",

author = "Praneeth, {N. S.K.H.} and Varma, {Naveen M.} and Naik, {Roshan Ramakrishna}",

year = "2017",

month = jan,

day = "5",

doi = "10.1109/RTEICT.2016.7808050",

language = "English",

pages = "1344--1350",

booktitle = "2016 IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2016 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "1st IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2016 ; Conference date: 20-05-2016 Through 21-05-2016",

}

Praneeth, NSKH, Varma, NM & Naik, RR 2017, Principle component analysis based intrusion detection system using support vector machine. in 2016 IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2016 - Proceedings., 7808050, Institute of Electrical and Electronics Engineers Inc., pp. 1344-1350, 1st IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2016, Bangalore, India, 20-05-16. https://doi.org/10.1109/RTEICT.2016.7808050

Principle component analysis based intrusion detection system using support vector machine. / Praneeth, N. S.K.H.; Varma, Naveen M.; Naik, Roshan Ramakrishna.
2016 IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1344-1350 7808050.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Principle component analysis based intrusion detection system using support vector machine

AU - Praneeth, N. S.K.H.

AU - Varma, Naveen M.

AU - Naik, Roshan Ramakrishna

PY - 2017/1/5

Y1 - 2017/1/5

N2 - Whenever an intrusion happens, privacy and security of the system are compromised. In order to detect different types of attacks that happen in a network, Intrusion Detection System (IDS) plays a crucial role in Network security. IDS is designed in order to classify the activities of the system into abnormal and normal. Machine learning based Intrusion Detection is gaining attention in recent years and is able to give better results with greater accuracy and high detection rate on novel attacks. In this paper, performance of different kernels of Support Vector Machine (SVM) are evaluated against Knowledge Discovery in Databases Cup'99(KDD) data set and detection accuracy, detection time are compared. The detection time is reduced by adopting Principal Component Analysis (PCA) which curtails higher dimensional dataset to lower dimensional dataset. The experiments which are conducted in this research shows that Gaussian Radial Basis Function kernel of SVM has higher detection accuracy.

AB - Whenever an intrusion happens, privacy and security of the system are compromised. In order to detect different types of attacks that happen in a network, Intrusion Detection System (IDS) plays a crucial role in Network security. IDS is designed in order to classify the activities of the system into abnormal and normal. Machine learning based Intrusion Detection is gaining attention in recent years and is able to give better results with greater accuracy and high detection rate on novel attacks. In this paper, performance of different kernels of Support Vector Machine (SVM) are evaluated against Knowledge Discovery in Databases Cup'99(KDD) data set and detection accuracy, detection time are compared. The detection time is reduced by adopting Principal Component Analysis (PCA) which curtails higher dimensional dataset to lower dimensional dataset. The experiments which are conducted in this research shows that Gaussian Radial Basis Function kernel of SVM has higher detection accuracy.

UR - http://www.scopus.com/inward/record.url?scp=85014994215&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85014994215&partnerID=8YFLogxK

U2 - 10.1109/RTEICT.2016.7808050

DO - 10.1109/RTEICT.2016.7808050

M3 - Conference contribution

AN - SCOPUS:85014994215

SP - 1344

EP - 1350

BT - 2016 IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2016 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 1st IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2016

Y2 - 20 May 2016 through 21 May 2016

ER -

Praneeth NSKH, Varma NM, Naik RR. Principle component analysis based intrusion detection system using support vector machine. In 2016 IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1344-1350. 7808050 doi: 10.1109/RTEICT.2016.7808050

Principle component analysis based intrusion detection system using support vector machine

Abstract

Conference

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this