Machine learning algorithms in context of intrusion detection

Mehmood, T. and Rais, H.B.Md. (2016) Machine learning algorithms in context of intrusion detection. In: UNSPECIFIED.

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

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.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Impact Factor: cited By 26
Uncontrolled Keywords: Artificial intelligence; Complex networks; Computer crime; Information science; Learning algorithms; Learning systems; Mercury (metal), Anomaly based detection; Anomaly detection; False positive rates; Intrusion detection method; Intrusion Detection Systems; Machine learning techniques; Network based intrusion detection systems; Supervised algorithm, Intrusion detection
Depositing User: Ms Sharifah Fahimah Saiyed Yeop
Date Deposited: 25 Mar 2022 06:55
Last Modified: 25 Mar 2022 06:55
URI: http://scholars.utp.edu.my/id/eprint/30473

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