Machine learning algorithms in context of intrusion detection

Design of efficient, accurate, and low complexity intrusion detection system is a challenging task. Intrusion detection method is a core of intrusion detection system and it can be either signature based or anomaly based. Although, signature based has high detection rate but it cannot detect novel a...

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Main Authors: Mehmood, T., Rais, H.B.Md.
Format: Conference or Workshop Item
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.30473 /
Published: Institute of Electrical and Electronics Engineers Inc. 2016
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010441018&doi=10.1109%2fICCOINS.2016.7783243&partnerID=40&md5=37d80b88b2489ef334bfbdc307b17227
http://eprints.utp.edu.my/30473/
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spelling utp-eprints.304732022-03-25T06:55:29Z Machine learning algorithms in context of intrusion detection Mehmood, T. Rais, H.B.Md. Design of efficient, accurate, and low complexity intrusion detection system is a challenging task. Intrusion detection method is a core of intrusion detection system and it can be either signature based or anomaly based. Although, signature based has high detection rate but it cannot detect novel attacks. Asymmetrically, anomaly based detection method can detect novel attacks but it has high false positive rate. Many machine learning techniques have been developed to cope with this problem. These machine learning algorithms develop a detection model in a training phase. This paper compares different supervised algorithms for the anomaly-based detection technique. The algorithms have been applied on the KDD99 dataset, which is the benchmark dataset used for anomaly-based detection technique. The result shows that not a single algorithm has a high detection rate for each class of KDD99 dataset. The performance measures used in this comparison are true positive rate, false positive rate, and precision. © 2016 IEEE. Institute of Electrical and Electronics Engineers Inc. 2016 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010441018&doi=10.1109%2fICCOINS.2016.7783243&partnerID=40&md5=37d80b88b2489ef334bfbdc307b17227 Mehmood, T. and Rais, H.B.Md. (2016) Machine learning algorithms in context of intrusion detection. In: UNSPECIFIED. http://eprints.utp.edu.my/30473/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description Design of efficient, accurate, and low complexity intrusion detection system is a challenging task. Intrusion detection method is a core of intrusion detection system and it can be either signature based or anomaly based. Although, signature based has high detection rate but it cannot detect novel attacks. Asymmetrically, anomaly based detection method can detect novel attacks but it has high false positive rate. Many machine learning techniques have been developed to cope with this problem. These machine learning algorithms develop a detection model in a training phase. This paper compares different supervised algorithms for the anomaly-based detection technique. The algorithms have been applied on the KDD99 dataset, which is the benchmark dataset used for anomaly-based detection technique. The result shows that not a single algorithm has a high detection rate for each class of KDD99 dataset. The performance measures used in this comparison are true positive rate, false positive rate, and precision. © 2016 IEEE.
format Conference or Workshop Item
author Mehmood, T.
Rais, H.B.Md.
spellingShingle Mehmood, T.
Rais, H.B.Md.
Machine learning algorithms in context of intrusion detection
author_sort Mehmood, T.
title Machine learning algorithms in context of intrusion detection
title_short Machine learning algorithms in context of intrusion detection
title_full Machine learning algorithms in context of intrusion detection
title_fullStr Machine learning algorithms in context of intrusion detection
title_full_unstemmed Machine learning algorithms in context of intrusion detection
title_sort machine learning algorithms in context of intrusion detection
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2016
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010441018&doi=10.1109%2fICCOINS.2016.7783243&partnerID=40&md5=37d80b88b2489ef334bfbdc307b17227
http://eprints.utp.edu.my/30473/
_version_ 1741197412436803584
score 11.62408

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