• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.1
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• pp.27-35
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• 2022

In this paper we propose white noise adding method to prevent missclassification of deep learning system by adversarial attacks. The proposed method is that adding white noise to input image that is benign or adversarial example. The experimental results are showing that the proposed method is robustness to 3 adversarial attacks such as FGSM attack, BIN attack and CW attack. The recognition accuracies of Resnet model with 18, 34, 50 and 101 layers are enhanced when white noise is added to test data set while it does not affect to classification of benign test dataset. The proposed model is applicable to defense to adversarial attacks and replace to time- consuming and high expensive defense method against adversarial attacks such as adversarial training method and deep learning replacing method.

• Journal of the Korea Institute of Military Science and Technology
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• v.19 no.6
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• pp.690-697
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• 2016

EOIS(electro-optical imaging system) is the main target of the laser weapon. Specially, the image sensor will be vulnerable because EOIS focuses the incident laser beam onto the image sensor. Accordingly, the laser-induced damage of the image sensor needs to be identified for the counter-measure against the laser attack. In this study, the laser-induced damage of the CCD image sensor irradiated by the CW(continuous wave) NIR(near infrared) laser was experimentally investigated and mechanisms of those damage occurrences were analyzed. In the experiment, the near infrared CW fiber laser was used as a laser source. As the fluence, which is the product of the irradiance and the irradiation time, increased, the permanent damages such as discoloration and breakdown appeared sequentially. The discoloration occurred when the color filter was damaged and then the breakdown occurred when the photodiode and substrate were damaged. From the experimental results, LIDTs(laser-induced damage thresholds) of damages were roughly determined.

• The KIPS Transactions:PartC
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• v.17C no.1
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• pp.1-14
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• 2010

IEEE 802.15.4[1] has been standardized for the physical layer and MAC layer of LR-PANs(Low Rate-Wireless Personal Area Networks) as a technology for operations with low power on sensor networks. The standardization is applied to the variety of applications in the shortrange wireless communication with limited output and performance, for example wireless sensor or virtual wire, but it includes vulnerabilities for various attacks because of the lack of security researches. In this paper, we analyze the vulnerabilities against the denial of sleep attacks on the MAC layer of IEEE 802.15.4, and propose a detection mechanism against it. In results, we analyzed the possibilities of denial of sleep attacks by the modification of superframe, the modification of CW(Contention Window), the process of channel scan or PAN association, and so on. Moreover, we comprehended that some of these attacks can mount even though the standardized security services such as encryption or authentication are performed. In addition to, we model for denial of sleep attacks by Beacon/Association Request messages, and propose a detection mechanism against them. This detection mechanism utilizes the management table consisting of the interval and node ID of request messages, and signal strength. In simulation results, we can show the effect of attacks, the detection possibility and performance superiorities of proposed mechanism.