• The Journal of the Korea Contents Association
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• v.10 no.5
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• pp.45-51
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• 2010

Appearance of a cloned AP(Access Point) causes MS(Mobile Station) to break an association with a normal AP(Access Point). If signal power of the cloned AP is stronger than that of the normal AP, MS associates with the cloned AP. Therefore, MS is easily exposed to attackers who installed the cloned AP. In this paper, we distinguish cloned AP from normal AP by using the association time and frame sequence number between normal AP and MS, then isolates the cloned AP. The simulation by NS-2 shows that our mechanism isolates efficiently a cloned AP and builds safer wireless LAN environment.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.427-428
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• 2009

무선 네트워크 환경에서 합법적인 AP(Access Point)의 MAC 주소, SSID(Service Set Identifier), 채널등의 정보를 이용하여 복제된 AP(Cloned Access Point)를 만들 수 있다. 복제된 AP는 합법적인 AP와 연결되어 있는 무선 스테이션들의 연결 설정을 끊고 자신과 연결 설정을 하게 한다. 무선 스테이션들이 복제된 AP와 통신을 하게 되면서 많은 공격으로부터 노출되게 된다. 본 연구에서는 복제된 AP가 설치되었을 때 무선 스테이션들이 합법적이 AP의 비콘 프레임과 복제된 AP 비콘 프레임의 시퀀스 번호를 이용하여 복제된 AP을 판별하였다. 시뮬레이터 NS-2를 이용하여 실험한 결과 본 논문에서 제안하는 메커니즘을 통해 무선 스테이션들이 복제된 AP의 등장을 판별할 수 있게 되어 보다 안전한 무선랜 환경을 구축할 수 있게 되었다.

• Journal of Life Science
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• v.28 no.9
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• pp.1007-1015
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• 2018

The E6-associated protein (E6AP) is known to induce the ubiquitination and proteasomal degradation of HCV core protein and thereby directly impair capsid assembly, resulting in a decline in HCV replication. To counteract this anti-viral host defense system, HCV core protein has evolved a strategy to inhibit E6AP expression via DNA methylation. In the present study, we further explored the mechanism by which HCV core protein inhibits E6AP expression. HCV core protein upregulated both the protein levels and enzyme activities of DNA methyltransferase 1 (DNMT1), DNMT3a, and DNMT3b to inhibit E6AP expression via promoter hypermethylation in HepG2 cells but not in Hep3B cells, which do not express p53. Interestingly, p53 overexpression alone in Hep3B cells was sufficient to activate DNMTs in the absence of HCV core protein and thereby inhibit E6AP expression via promoter hypermethylation. In addition, upregulation of p53 was absolutely required for the HCV core protein to inhibit E6AP expression via promoter hypermethylation, as evidenced by both p53 knockdown and ectopic expression experiments. Accordingly, levels of the ubiquitinated forms of HCV core protein were lower in HepG2 cells than in Hep3B cells. Based on these observations, we conclude that HCV core protein evades ubiquitin-dependent proteasomal degradation in a p53-dependent manner.

• Korean Journal of Microbiology
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• v.30 no.3
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• pp.218-224
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• 1992

Recombinant plasmid shuttle vectors were constructed for genetic studies on the oxidation of carbon monoxide by carboxydobacteria. Two vectors. pYK322 (7.2 kb, Ap'. Tc') and pYK 324 (7.2 kb, Ap', Tc'), were constructed using pBR322 and pYK100. a small plasmid in Pseudomonas carbo,xydovorans. Four plasmids. pYK2IO (5.2 kb. Cm'), pYK220 (5.2 kb, Cmr), pYK230 (5.2 kb, Cm'), and pYK232 (5.2 kb. Cm'), were constructed using pACYC184 and pYK100. Transformation of several carboxydobacteria with pYK322 and pYK220 was round to be efficient when the cells were transformed by the methoti of Bagdasarian and Timmis (Curr. Top. Microbiol. Immunol. 96:47-67. 1982) with several modifications; cells growing on 0.2% succinate were harvested at the mid-exponential phase. 10 mM RbCl in transformation solution was substituted with 100 mM KCI. cclls in transformation solution were incubated for 12 h at 4'C before addition of DNA and heat shock was carried out for 3 min at 45$^{\circ}$C. Plasmid vectors used for transformation, however. were not detected from antibiotics-resistant transformants, suggesting that the vectors may be integrated into the chromosomal DNA.

• Journal of Broadcast Engineering
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• v.27 no.3
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• pp.391-401
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• 2022

As deep learning-based object detection and recognition research have been developed recently, the scope of application to industry and real life is expanding. But deep learning-based systems in the construction system are still much less studied. Calculating materials in the construction system is still manual, so it is a reality that transactions of wrong volumn calculation are generated due to a lot of time required and difficulty in accurate accumulation. A fast and accurate automatic drawing recognition system is required to solve this problem. Therefore, we propose an AI-based automatic drawing recognition accumulation system that detects and recognizes steel materials in construction drawings. To accurately detect steel materials in construction drawings, we propose data augmentation techniques and spatial attention modules for improving small object detection performance based on YOLOv4. The detected steel material area is recognized by text, and the number of steel materials is integrated based on the predicted characters. Experimental results show that the proposed method increases the accuracy and precision by 1.8% and 16%, respectively, compared with the conventional YOLOv4. As for the proposed method, Precision performance was 0.938. The recall was 1. Average Precision AP_0.5 was 99.4% and AP_0.5:0.95 was 67%. Accuracy for character recognition obtained 99.9.% by configuring and learning a suitable dataset that contains fonts used in construction drawings compared to the 75.6% using the existing dataset. The average time required per image was 0.013 seconds in the detection, 0.65 seconds in character recognition, and 0.16 seconds in the accumulation, resulting in 0.84 seconds.