On the Feasibility of Deep Learning in Sensor Network Intrusion Detection

Project Code :TCMAPY221

Objective

Our goal is to predict the solution for wireless sensor network by using intrusion detection system (IDS) is a solution to this problem. It analyzes the network by collecting sufficient amount of data and detects abnormal behavior of sensor node(s). IDS based security mechanisms proposed for other network paradigms such as ad hoc networks, cannot directly be used in WSNs.

Abstract

In this project, we present a comprehensive analysis of the use of machine and deep learning solutions for IDS systems in Wireless Sensor Networks (WSNs). To accomplish this, we introduce Restricted Boltzmann Machine-based Clustered IDS (RBC-IDS), a potential deep learning-based IDS methodology for monitoring critical infrastructures by WSNs.

We study the performance of RBC-IDS, and compare it to the previously proposed adaptive machine learning-based IDS: The Adaptively Supervised and Clustered Hybrid IDS (ASCH-IDS). Numerical results show that RBC-IDS and ASCH-IDS achieve the same detection and accuracy rates, though the detection time of RBCIDS is approximately twice that of ASCH-IDS.

Keywords: Wireless Sensor Network, Cyber Security, Restricted Boltzmann Machine, Deep Learning, Machine Learning, Intrusion Detection.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.