Paper Info
Title
Utilizing Blockchain for Distributed Machine Learning based Intrusion Detection in Internet of Things
Abstract
In this paper, we present a distributed machine learning based intrusion detection system in Internet of Things (IoT) utilizing Blockchain technology. In particular, spectral partitioning is proposed to divide the IoT network into autonomous systems (AS) enabling traffic monitoring for intrusion detection (ID) to be performed by the selected AS border area nodes in a distributed manner. The ID system is based on machine learning, where a support-vector machine algorithm is trained using prominent IoT data sets and detection of the attackers is provided. Furthermore, the integrity of the attackers' list is offered by utilizing Blockchain technology, which enables a distributed sharing of the attackers' information among the AS border area nodes of the Blockchain network. Simulations are performed to evaluate different aspects of the proposed IoT system and demonstrate the potential of integrating machine learning based ID to a distributed spectral partitioned Blockchain network.
Year
DOI
Venue
2020
10.1109/DCOSS49796.2020.00074
2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS)
Keywords
DocType
ISSN
Blockchain,Spectral Partitioning,Machine Learning,Intrusion Detection,Internet of Things
Conference
2325-2936
ISBN
Citations 
PageRank 
978-1-7281-9804-0
0
0.34
References 
Authors
9
4
Name
Order
Citations
PageRank
Muhammad Asaad Cheema100.34
Hassaan Khaliq Qureshi29518.16
Chrysostomos Chrysostomou311312.65
Marios Lestas412017.84