Anomaly-Based Intrusion Detection using Extreme Learning Machine and Aggregation of Network Traffic Statistics in Probability Space

Abstract

Recently, with the Increased Use of Network Communication, the Risk of Compromising the Information Has Grown Immensely. Intrusions Have Become More Sophisticated and Few Methods Can Achieve Efficient Results While the Network Behavior Constantly Changes. This Paper Proposes an Intrusion Detection System based on Modeling Distributions of Network Statistics and Extreme Learning Machine (Elm) to Achieve High Detection Rates of Intrusions. the Proposed Model Aggregates the Network Traffic at the Ip Subnetwork Level and the Distribution of Statistics Are Collected for the Most Frequent Ipv4 Addresses Encountered as Destination. the Obtained Probability Distributions Are Learned by Elm. This Model is Evaluated on the Iscx-Ids 2012 Dataset, Which is Collected using a Real-Time Testbed. the Model is Compared Against Leading Approaches using the Same Dataset. Experimental Results Show that the Presented Method Achieves an Average Detection Rate of 91% and a Misclassification Rate of 9%. the Experimental Results Show that Our Methods Significantly Improve the Performance of the Simple Elm Despite a Trade-Off between Performance and Time Complexity. Furthermore, Our Methods Achieve Good Performance in Comparison with the Other Few State-Of-The-Art Approaches Evaluated on the Iscx-Ids 2012 Dataset.

Department(s)

Engineering Management and Systems Engineering

Comments

Horizon 2020 Framework Programme, Grant 737422

Keywords and Phrases

Extreme learning machine; Hierarchical clustering; Intrusion detection; Network behavior analysis; Probability density function

International Standard Serial Number (ISSN)

1866-9964; 1866-9956

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Springer, All rights reserved.

Publication Date

01 Oct 2018

Link to Full Text

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