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.
| Original language | English |
|---|---|
| Title of host publication | Developing and Monitoring Smart Environments for Intelligent Cities |
| Publisher | IGI Global |
| Pages | 300-322 |
| ISBN (Print) | 9781799850625 |
| DOIs | |
| Publication status | Published - Nov 2020 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
-
SDG 11 Sustainable Cities and Communities
Keywords
- Automation
- Intrusion detection
- Machine learning
- Naïve Bayes Technique
- Network security
- Rapid urbanisation
- Smart city
- Smart home
Fingerprint
Dive into the research topics of 'Hybrid Intrusion Detection System for Smart Home Applications'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver