Detecting Impersonation Attacks in a Static WSN
(2017) EITM01 20171Department of Electrical and Information Technology
- Abstract
- The current state of security found in the IoT domain is highly flawed, a major problem being that the cryptographic keys used for authentication can be easily extracted and thus enable a myriad of impersonation attacks. In this MSc thesis a study is done of an authentication mechanism called device fingerprinting. It is a mechanism which can derive the identity of a device without relying on device identity credentials and thus detect credential-based impersonation attacks.
A proof of concept has been produced to showcase how a fingerprinting system can be designed to function in a resource constrained IoT environment. A novel approach has been taken where several fingerprinting techniques have been combined through machine learning to... (More) - The current state of security found in the IoT domain is highly flawed, a major problem being that the cryptographic keys used for authentication can be easily extracted and thus enable a myriad of impersonation attacks. In this MSc thesis a study is done of an authentication mechanism called device fingerprinting. It is a mechanism which can derive the identity of a device without relying on device identity credentials and thus detect credential-based impersonation attacks.
A proof of concept has been produced to showcase how a fingerprinting system can be designed to function in a resource constrained IoT environment. A novel approach has been taken where several fingerprinting techniques have been combined through machine learning to improve the system’s ability to deduce the identity of a device.
The proof of concept yields high performant results, indicating that fingerprinting techniques are a viable approach to achieve security in an IoT system. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8927499
- author
- Selleby, Viktor LU and Lin, Anton LU
- supervisor
-
- Martin Hell LU
- organization
- course
- EITM01 20171
- year
- 2017
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- Device Fingerprinting, IoT, Machine Learning, Machine to Machine Authentication, Impersonation Attacks, Spoofing Attacks, Wireless Sensor Network
- report number
- LU/LTH-EIT 2017-603
- language
- English
- id
- 8927499
- date added to LUP
- 2017-10-20 08:05:40
- date last changed
- 2017-10-20 08:05:40
@misc{8927499,
abstract = {{The current state of security found in the IoT domain is highly flawed, a major problem being that the cryptographic keys used for authentication can be easily extracted and thus enable a myriad of impersonation attacks. In this MSc thesis a study is done of an authentication mechanism called device fingerprinting. It is a mechanism which can derive the identity of a device without relying on device identity credentials and thus detect credential-based impersonation attacks.
A proof of concept has been produced to showcase how a fingerprinting system can be designed to function in a resource constrained IoT environment. A novel approach has been taken where several fingerprinting techniques have been combined through machine learning to improve the system’s ability to deduce the identity of a device.
The proof of concept yields high performant results, indicating that fingerprinting techniques are a viable approach to achieve security in an IoT system.}},
author = {{Selleby, Viktor and Lin, Anton}},
language = {{eng}},
note = {{Student Paper}},
title = {{Detecting Impersonation Attacks in a Static WSN}},
year = {{2017}},
}