• Journal of information and communication convergence engineering
• /
• v.1 no.1
• /
• pp.39-43
• /
• 2003

The technology required to advance the state of the art of ultra-high-intensity excimer amplifier construction to the 100 J/100fs output pulse level is identified. The preliminary design work for very large final amplifier pumped by electron beam module is described, and key design problems and approaches are presented and discussed in detail based on the recent experimental and theoretical results.

• Journal of Computing Science and Engineering
• /
• v.11 no.4
• /
• pp.152-159
• /
• 2017

Three-dimensional (3D) printing is applied to many areas of life, but 3D printing models are stolen by pirates and distributed without any permission from the original providers. Moreover, some special models and anti-weapon models in 3D printing must be secured from the unauthorized user. Therefore, 3D printing models must be encrypted before being stored and transmitted to ensure access and to prevent illegal copying. This paper presents a selective encryption algorithm for 3D printing models based on clustering and the frequency domain of discrete cosine transform. All facets are extracted from 3D printing model, divided into groups by the clustering algorithm, and all vertices of facets in each group are transformed to the frequency domain of a discrete cosine transform. The proposed algorithm is based on encrypting the selected coefficients in the frequency domain of discrete cosine transform to generate the encrypted 3D printing model. Experimental results verified that the proposed algorithm is very effective for 3D printing models. The entire 3D printing model is altered after the encryption process. The decrypting error is approximated to be zero. The proposed algorithm provides a better method and more security than previous methods.

• International Journal of Computer Science & Network Security
• /
• v.21 no.8
• /
• pp.182-186
• /
• 2021

The market for smart phones has been booming in the past few years. There are now over 400,000 applications on the Android market. Over 10 billion Android applications have been downloaded from the Android market. Due to the Android popularity, there are now a large number of malicious vendors targeting the platform. Many honest end users are being successfully hacked on a regular basis. In this work, a cloud based reputation security model has been proposed as a solution which greatly mitigates the malicious attacks targeting the Android market. Our security solution takes advantage of the fact that each application in the android platform is assigned a unique user id (UID). Our solution stores the reputation of Android applications in an anti-malware providers' cloud (AM Cloud). The experimental results witness that the proposed model could well identify the reputation index of a given application and hence its potential of being risky or not.

• Proceedings of the IEEK Conference
• /
• 2006.06a
• /
• pp.297-298
• /
• 2006

In this paper, we propose a real-time surveillance system for security of important area such as military bases, prisons, and strategic infra structures. The proposed system recognizes the movement of objects in dark environments. First, the Multi-scale retinex (MSR) is processed to enhance the contrast of image captured in dark environments. Then, the enhanced input image is subtracted with the background image. Finally, each bounding box enclosing each objects are tracked. The center point of each bounding box obtained by the proposed algorithm provides more accurate tracking information. Experimental results show that the proposed system provides good performance even though an object moves very fast and the background is quite dark.

• Journal of information and communication convergence engineering
• /
• v.22 no.3
• /
• pp.199-206
• /
• 2024

The network security of Plug-and-Charge (PnC) technology in electric vehicle charging systems is typically achieved through the well-known Transport Layer Security (TLS) protocol, which causes high communication overhead. To reduce this overhead, a differential authentication method employing different schemes for individual users has been proposed. However, decisions use a simple threshold approach and no quantitative performance evaluation should be made. In this study, we determined each user's trust using several machine learning algorithms with their charging patterns and compared them. The experimental results reveal that the proposed approach outperforms the conventional approach by 41.36% in terms of round-trip time efficiency, demonstrating its effectiveness in reducing the TLS overhead. In addition, we show the simulation results for three user authentication methods and capture the performance variations under CPU busy waiting scenarios.

• Journal of the Optical Society of Korea
• /
• v.14 no.4
• /
• pp.305-309
• /
• 2010

Photopolymer films containing fluorescent anthracene polymer, polymethyleneanthracene (PMAn), were prepared with different concentrations of PMAn for holographic recording useful for security documents. The fluorescent photopolymer film showed enhanced fluorescent intensity due to the micro-separation which arose from grating formation and diffusion during photopolymerization. Experimental values of diffraction efficiency were well matched to the simulated values for photopolymers having different PMAn concentrations. Holography patterning was carried out using the fluorescent photopolymer under a photo-mask. A grating was confirmed using microscope techniques in the recorded area under the pattern. Importantly the recorded area showed enhanced fluorescence compared to the unrecorded part, allowing fluorescence patterns at micro scale along with the submicron grating pattern. The fluorescence pattern recorded on the photopolymer film provides additional readability of holographic reading and thus is useful for secure recording and reading of information.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.1
• /
• pp.240-259
• /
• 2020

Analyzing network traffic is the basis of dealing with network security issues. Most of the network security systems depend on the feature selection of network traffic data and the detection ability of malicious traffic in network can be improved by the correct method of feature selection. An FAFS method, which is short for Fuzzy Association Feature Selection method, is proposed in this paper for network malicious traffic detection. Association rules, which can reflect the relationship among different characteristic attributes of network traffic data, are mined by association analysis. The membership value of association rules are obtained by the calculation of fuzzy reasoning. The data features with the highest correlation intensity in network data sets are calculated by comparing the membership values in association rules. The dimension of data features are reduced and the detection ability of malicious traffic detection algorithm in network is improved by FAFS method. To verify the effect of malicious traffic feature selection by FAFS method, FAFS method is used to select data features of different dataset in this paper. Then, K-Nearest Neighbor algorithm, C4.5 Decision Tree algorithm and Naïve Bayes algorithm are used to test on the dataset above. Moreover, FAFS method is also compared with classical feature selection methods. The analysis of experimental results show that the precision and recall rate of malicious traffic detection in the network can be significantly improved by FAFS method, which provides a valuable reference for the establishment of network security system.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.11
• /
• pp.5594-5615
• /
• 2019

Malicious social robots, which are disseminators of malicious information on social networks, seriously affect information security and network environments. The detection of malicious social robots is a hot topic and a significant concern for researchers. A method based on classification has been widely used for social robot detection. However, this method of classification is limited by an unbalanced data set in which legitimate, negative samples outnumber malicious robots (positive samples), which leads to unsatisfactory detection results. This paper proposes the use of generative adversarial networks (GANs) to extend the unbalanced data sets before training classifiers to improve the detection of social robots. Five popular oversampling algorithms were compared in the experiments, and the effects of imbalance degree and the expansion ratio of the original data on oversampling were studied. The experimental results showed that the proposed method achieved better detection performance compared with other algorithms in terms of the F1 measure. The GAN method also performed well when the imbalance degree was smaller than 15%.

• Journal of Korea Multimedia Society
• /
• v.20 no.2
• /
• pp.244-253
• /
• 2017

Reversible DNA watermarking is capable of continuous DNA storage and forgery prevention, and has the advantage of being able to analyze biological mutation processes by external watermarking by iterative process of concealment and restoration. In this paper, we propose a reversible DNA watermarking method based on histogram multiple shifting of noncoding DNA sequence that can prevent false start codon, maintain original sequence length, maintain high watermark capacity without biologic mutation. The proposed method transforms the non-coding region DNA sequence to the n-th code coefficients and embeds the multiple bits of the n-th code coefficients by the non-recursive histogram multiple shifting method. The multi-bit embedding process prevents the false start codon generation through comparison search between adjacent concealed nucleotide sequences. From the experimental results, it was confirmed that the proposed method has higher watermark capacity of 0.004-0.382 bpn than the conventional method and has higher watermark capacity than the additional data. Also, it was confirmed that false start codon was not generated unlike the conventional method.

• Journal of Korea Multimedia Society
• /
• v.21 no.1
• /
• pp.61-68
• /
• 2018

In this paper, we present an effective encryption algorithm for 3D printing models in the frequency domain of discrete cosine transform to prevent illegal copying, access in the secured storage and transmission. Facet data of 3D printing model is extracted to construct a three by three matrix that is then transformed to the frequency domain of discrete cosine transform. The proposed algorithm is based on encrypting the DC coefficients of matrixes of facets in the frequency domain of discrete cosine transform in order to generate the encrypted 3D printing model. Experimental results verified that the proposed algorithm is very effective for 3D printing models. The entire 3D printing model is altered after the encryption process. The proposed algorithm is provide a better method and more security than previous methods.