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to further auditions. All data into the ledger become server, resulting in a Single Point of Failure (SPF). It
immutable, and no mechanism can modify it. is vulnerable to the second scenario due to the serv-
er database accept modifications on stored data. It is
Security Analysis also vulnerable to the third scenario due to its lack of
Fault Tolerance mechanisms. Finally, it is vulnerable
This section presents a security analysis compar- to the last scenario due to its lack of authentication on
ing a conventional MMS (with centralized and de- UAV-Operator communications.
centralized databases) and a blockchain-based MMS The decentralized database MMS differs from the
regarding the attack model described in Section III-B. centralized one just in the first attack scenario due to
Table I summarizes the analysis result, labeling with a its distributed data storage. If an attacker successfully
red lock if the system is vulnerable to the attack and break in a single ND, data would be safe in the other
with a green lock if the system is resilient to the attack, servers. However, in the other three scenarios, the de-
also presenting the main reason for the success or fail- centralized MMS would perform like the centralized
ure of the system security in each scenario.
one.
The blockchain-based MMS is resilient against
TABLE I: Security Analysis all four attack scenarios. It resists the first scenario
attack due to its Distributed Ledger Technology store
the sensing data in different nodes, unsettling the SPF.
It resists the second scenario because all data added
to the ledger becomes immutable and the attacker no
longer can modify stored data. It resists the collusion
attack if the attacker corrupts less than 1/3 or 1/2 (de-
pending on the consensus protocol) of all blockchain
nodes due to the Fault Tolerance mechanism of the
consensus protocol. And finally, it also resists the last
attack scenario, securing UAV-Operator communica-
tions through asymmetric cryptography and the MSP
key management.

EXPERIMENT AND RESULTS
Firstly, we analyze the conventional centralized
database MMS. It is vulnerable to the first attack sce- In this section, we quantitatively analyze the pro-
nario due to the storage of sensing data in only one cessing overhead caused by the blockchain technolo-
gy through an experiment integrating our blockchain
prototype with the Bra-
zilian Navy low-cost AIS
system. To do so, we
evaluate the prototype
operation since data
acquisition on sensing
nodes until its insertion
on the blockchain’s ledg-
er. Figure 3 illustrates
the experiment envi-
ronment, showing the
blockchain client con-
nected to our virtualized
server and on top of it,
each blockchain entity
Fig. 3: Experiment Environment

CIAW – EFICIÊNCIA, CULTURA E TRADIÇÃO 79