A Novel Ensemble Method for Advanced Intrusion Detection in Wireless Sensor Networks

Document Type

Conference Proceeding

Source of Publication

IEEE International Conference on Communications

Publication Date

6-1-2020

Abstract

© 2020 IEEE. With the increase of cyber attack risks on critical infrastructures monitored by networked systems, robust Intrusion Detection Systems (IDSs) for protecting the information have become vital. Designing an IDS that performs with maximum accuracy with minimum false alarms is a challenging task. Ensemble method considered as one of the main developments in machine learning in the past decade, it finds an accurate classifier by combining many classifiers. In this paper, an ensemble classification procedure is proposed using Random Forest (RF), DensityBased Spatial Clustering of Applications with Noise (DBSCAN) and Restricted Boltzmann Machine (RBM) as base classifiers. RF, DBSCAN, and RBM techniques have been used for classification purposes. The ensemble model is introduced for achieving better results. Bayesian Combination Classification (BCC) has been adopted as a combination technique. Independent BCC (IBCC) and Dependent BCC (DBCC) have been tested for performance comparison. The model shows a promising result for all classes of attacks. DBCC performs over IBCC in terms of accuracy and detection rates. Through simulations under a wireless sensor network scenario, we have verified that DBCC-based IDS works with \approx 100\% detection and \approx 1.0 accuracy rate in the existence of intrusive behavior in the tested Wireless Sensor Network (WSN).

ISBN

9781728150895

ISSN

1550-3607

Publisher

Institute of Electrical and Electronics Engineers Inc.

Volume

2020-June

Last Page

6

Disciplines

Computer Sciences

Keywords

Ensemble Learning, Intrusion Detection, Machine learning, Wireless Sensor Networks.

Scopus ID

85089425641

Indexed in Scopus

yes

Open Access

no

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