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Detecting Impersonation Attacks in a Static WSN

Selleby, Viktor LU and Lin, Anton LU (2017) EITM01 20171
Department 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:
author
Selleby, Viktor LU and Lin, Anton LU
supervisor
organization
course
EITM01 20171
year
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},
  keyword      = {Device Fingerprinting,IoT,Machine Learning,Machine to Machine Authentication,Impersonation Attacks,Spoofing Attacks,Wireless Sensor Network},
  language     = {eng},
  note         = {Student Paper},
  title        = {Detecting Impersonation Attacks in a Static WSN},
  year         = {2017},
}