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Title: Detecting Insider attacks in Blockchain Networks
Blockchain technology has recently gained high popularity in data security, primarily to mitigate against data breach and manipulation. Since its inception in 2008, it has been applied in different areas mainly to maintain data integrity and consistency. Blockchain has been tailored to secure data due to its data immutability and distributive technology. Despite the high success rate in data security, the inability to identify compromised insider nodes is one of the significant problems encountered in blockchain architectures. A Blockchain network is made up of nodes that initiate, verify and validate transactions. If compromised, these nodes can manipulate submitted transactions, inject fake transactions, or retrieve unauthorized information that might eventually compromise the stored data's integrity and consistency. This paper proposes a novel method of detecting these compromised blockchain nodes using a server-side authentication process and thwart their activities before getting updated in the blockchain ledger. In evaluating the proposed system, we perform four common insider attacks, which fall under the following three categories: (1)Those attacks targeting the Blockchain to bring it down. (2) the attacks that attempt to inject fake data into the database. (3) The attacks that attempt to hijack or retrieve unauthorized data. We described how we implement the attacks and how our architecture detects them before they impact the network. Finally, we displayed the attack detection time for each attack and compared our approach with other existing methods.  more » « less
Award ID(s):
1818884
NSF-PAR ID:
10289916
Author(s) / Creator(s):
;
Date Published:
Journal Name:
IEEE 2021 International Symposium on Networks, Computers and Communications: Trust, Security, and Privacy (ISNCC- 2021 TSP
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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