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Table 1. Performance Rates

Threshold TP FP TN FN

0.3 88.24% 92.31% 7.69% 11,76%
0.4 82.35% 15.38% 84.62% 17,65%
0.5 82.35% 0% 100% 17,65%

0.6 76.47% 0% 100% 23,53%
0.7 47.06% 0% 100% 52,94%

CONCLUSIONS
Fig. 5. Performance of the triggering mechanism
Considering the theoretical framework presented
but it is not detected. Based on the results, lowering and the simulations carried out, it is possible to realize
the threshold increases the TP rate, but also increases that EA and cyberattacks can be linked to each other,
the FP rate (which may cause fortuitous and unwanted forming a cyber-electronic attack capable of affecting
attack activations). On the other hand, increasing the naval radar systems. The attack exploits the fact that
threshold decreases the FP rate, but also decreases the the radar, as a sensor, can be considered an open door
TP rate (which reduces the attack effectiveness). Ac- for commands. It is possible to use image processing
cording to the results, the best threshold from the at- techniques to trigger a malicious code previously in-
tacker point of view is 0.5. Note that with this thresh- stalled on a naval radar system with a good accuracy
old the attacker is able to obtain the maximum TP rate and effectiveness, maintaining the due safety against
(82.35%) without false positives. It means that, with accidental activations. Even with all the information
this threshold, considering the evaluated scenarios, the security devices, all computer systems are subject to
probability of an accidental attack activation tends to the risk of being infected by malware. This mechanism
0% (which is important to avoid the attack disclosure) can be used for the benefit of a naval operation, be-
and the attacker has 82.35% of probability in suc- ing activated at the most opportune moment for the
cessfully activating the cyber component of the attack attacking force. For future work we plan to evaluate
in the first attempt. Note that, with two attempts the the performance of the proposed mechanism in a real
probability of having the attack properly activated in system and investigate countermeasures to mitigate
at least one of the attempts increases to 96.88%. this threat – such as tools to verify the integrity of the
software used in naval radars.

REFERENCES

[1] S. McLaughlin et al., “The Cybersecurity Landscape in Industrial Control Systems,” in Proceedings of
the IEEE, vol. 104, no. 5, pp. 1039-1057, May 2016.
[2] G. Liang et al., “The 2015 Ukraine Blackout: Implications for False Data Injection Attacks,” in IEEE
Transactions on Power Systems, vol. 32, no. 4, pp. 3317-3318, July 2017.
[3] A. O. de Sá, L. F. R da C. Carmo, R. C. S. Machado, “Bio-inspired Active System Identification: a Cyber-
physical Intelligence Attack in Networked Control Systems,” in Mobile Networks and Applications, pp. 1-14,
October 2017.
[4] R. Langner, “Stuxnet: Dissecting a Cyberwarfare Weapon,” in IEEE Security & Privacy, vol. 9, no. 3, pp.
49-51, May-June 2011.

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