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A Cyber-Physical System to Detect IoT Security Threats of a Smart Home Heterogeneous Wireless Sensor Node
- Source :
- IEEE Access, Vol 8, Pp 205989-206002 (2020)
- Publication Year :
- 2020
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2020.
-
Abstract
- The advent of the Internet of Things (IoT) allows the Cyber-Physical System (CPS) components to communicate with other devices, and to interact with safety-critical systems, posing new research challenges in security, privacy, and reliability. Efficient power measurement in smart IoT devices has become one of the key research topics. In this paper, we design and develop a CPS to detect IoT security threats via behavioral power profiling of a heterogeneous wireless sensor device using a Raspberry Pi and a smartphone. Experimentation and verification have been conducted on a group of smart IoT devices with different test scenarios, including the device in an idle and active state with distributed denial-of-service (DDoS) and a man-in-the-middle (MitM) attack. We propose to use the device power consumption rate to predict and detect a security threat using statistical signal processing and multivariate regression model. The proposed system can detect a potential security threat with an average accuracy of 80% and a device high of 89%.
- Subjects :
- Cybersecurity
General Computer Science
Computer science
Reliability (computer networking)
Denial-of-service attack
02 engineering and technology
Man-in-the-middle attack
01 natural sciences
power
Home automation
0202 electrical engineering, electronic engineering, information engineering
Wireless
Profiling (information science)
General Materials Science
Electrical and Electronic Engineering
business.industry
010401 analytical chemistry
General Engineering
Cyber-physical system
020206 networking & telecommunications
multivariate regression model
0104 chemical sciences
AI
Key (cryptography)
CPS
lcsh:Electrical engineering. Electronics. Nuclear engineering
business
lcsh:TK1-9971
IoCPT
Computer network
Subjects
Details
- ISSN :
- 21693536
- Volume :
- 8
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....9c71099bb30518135c3ef761d81bbe05
- Full Text :
- https://doi.org/10.1109/access.2020.3037032