It refers to statistical techniques used in machine learning for solving binary classification problems in which there is one or more independent variables that determines the dependent variable (outcome).
Published in Chapter:
Hybrid Intrusion Detection System for Smart Home Applications
Fida Hussain (Canterbury Christ Church University, UK), Abhaya Induruwa (Canterbury Christ Church University, UK), and Man Qi (Canterbury Christ Church University, UK)
Copyright: © 2021
|Pages: 23
DOI: 10.4018/978-1-7998-5062-5.ch012
Abstract
Smart homes, which incorporate IoT technologies to provide home security, efficient environmental services, conveniences, and improved living standards, are becoming the centre of smart urban developments. With the increased inter-connectivity of smart objects and sensors, there is now, also, an increased level of cyber threats, which can compromise privacy and security. These threats either modify packets of information or inject modified packets into the networks. This chapter examines current intrusion detection systems (IDSs) and presents a unique solution to overcome intrusion detection challenges. It discusses the implementation of smart home IDS (SHIDS), using a machine learning based signature and anomaly intrusion detection scheme to detect network intrusions in the smart home. Suggested mechanism is based on naïve Bayes technique to improve the detection performance. The performance of SHIDS has been tested with network intrusions resulting from DoS, probe, remote-to-local (R2L), and user-to-root (U2R) attacks.