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Forgery Detection Mechanism with Abnormal Structure Analysis on Office Open XML based MS-Word File
HanSeong Lee,Hyung-Woo Lee 한국인터넷방송통신학회 2019 Journal of Advanced Smart Convergence Vol.8 No.4
We examine the weaknesses of the existing OOXML-based MS-Word file structure, and analyze how data concealment and forgery are performed in MS-Word digital documents. In case of forgery by including hidden information in MS-Word digital document, there is no difference in opening the file with the MS-Word Processor. However, the computer system may be malfunctioned by malware or shell code hidden in the digital document. If a malicious image file or ZIP file is hidden in the document by using the structural vulnerability of the MS-Word document, it may be infected by ransomware that encrypts the entire file on the disk even if the MS-Word file is normally executed. Therefore, it is necessary to analyze forgery and alteration of digital document through internal structure analysis of MS-Word file. In this paper, we designed and implemented a mechanism to detect this efficiently and automatic detection software, and presented a method to proactively respond to attacks such as ransomware exploiting MS-Word security vulnerabilities.
The clinical impact of family history of cancer in female never-smoker lung adenocarcinoma
Lee, Youngjoo,Jeon, Jae Hyun,Goh, Sung-Ho,Roh, Hanseong,Yun, Ji-Young,Kwon, Nak-Jung,Choi, Jin Ho,Yang, Hee Chul,Kim, Moon Soo,Lee, Jong Mog,Lee, Geon Kook,Han, Ji-Youn Elsevier 2019 Lung cancer Vol.136 No.-
<P><B>Abstract</B></P> <P><B>Objectives</B></P> <P>Accumulating evidence reveals the association between the risk of never-smoker lung cancer and family history of cancer. However, the clinicogenomic effect of family history of cancer in never-smoker lung cancer remains unknown.</P> <P><B>Material and methods</B></P> <P>We screened 3,241 lung cancer patients who (a) underwent curative resection at National Cancer Center (Goyang, Korea) between 2001–2014, and (b) completed a pre-designed interview about family/smoking history at the time of diagnosis and identified 604 female never smoker lung adenocarcinoma. A positive family history of cancer [categorized as pulmonary cancer (FH-PC) or non-pulmonary cancer (FH-NPC)] was defined as a self-reported history of cancer in first-degree relatives. Survival data were followed up until January 2017. Multiplexed targeted next-generation sequencing was performed for genetic profiling.</P> <P><B>Results</B></P> <P>Of 604 patients, 29.1% (n = 176) had a FH, including 132 (21.9%) with FH-NPC and 44 (7.3%) with FH-PC. Patients with the FH-NPC had a higher proportion of young patients (≤45 years) than those without the FH-NPC (FH-NPC, FH-PC, and no FH; 13.6%, 2.3%, and 8.2%, respectively; <I>P</I> = 0.032). Patients with the FH-NPC had an increased risk of recurrence (hazard ratio [HR]: 1.90; 95% confidence interval [CI]: 1.40–2.56; <I>P<</I>0.001) and death (HR: 1.67; 95% CI: 1.18–2.37; <I>P=</I>0.004). In contrast, the FH-PC had no prognostic effect on recurrence (HR: 1.23; 95% CI: 0.71–2.15; <I>P = 0.456</I>) and death (HR: 0.93; 95% CI: 0.45–1.91; <I>P=</I>0.838). Among three driver oncogene alterations, <I>EGFR</I> mutation was significantly associated with the FH-PC (53.8%, 84.1%, and 65.8%, respectively; <I>P</I> = 0.016), <I>ALK</I>/<I>ROS1</I>/<I>RET</I> fusions was significantly associated with the FH-NPC (13.7%, 0.0%, and 5.0%, respectively; <I>P</I> = 0.004), but <I>KRAS</I> mutation was not associated with any type of the FH (13.8% vs. 6.0% vs. 7.8%, respectively; <I>P</I> = 0.288).</P> <P><B>Conclusion</B></P> <P>The type of family history of cancer was associated with distinct clinocogenomic subtypes and prognosis of never-smoker lung adenocarcinoma.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Family history of cancer is related to distinct subtypes of never smoker lung cancer. </LI> <LI> <I>ALK/ROS1/RET</I> fusions are enriched in patients with family history of nonlung cancer. </LI> <LI> <I>EGFR</I> mutations are enriched in patients with family history of lung cancer. </LI> <LI> Family history of nonlung cancer is associated with poor prognosis after operation. </LI> </UL> </P>
Forgery Detection Mechanism with Abnormal Structure Analysis on Office Open XML based MS-Word File
Lee, HanSeong,Lee, Hyung-Woo The Institute of Internet 2019 International journal of advanced smart convergenc Vol.8 No.4
We examine the weaknesses of the existing OOXML-based MS-Word file structure, and analyze how data concealment and forgery are performed in MS-Word digital documents. In case of forgery by including hidden information in MS-Word digital document, there is no difference in opening the file with the MS-Word Processor. However, the computer system may be malfunctioned by malware or shell code hidden in the digital document. If a malicious image file or ZIP file is hidden in the document by using the structural vulnerability of the MS-Word document, it may be infected by ransomware that encrypts the entire file on the disk even if the MS-Word file is normally executed. Therefore, it is necessary to analyze forgery and alteration of digital document through internal structure analysis of MS-Word file. In this paper, we designed and implemented a mechanism to detect this efficiently and automatic detection software, and presented a method to proactively respond to attacks such as ransomware exploiting MS-Word security vulnerabilities.
조한승(Hanseong Cho),이종화(Jonghwa Lee),유재석(Jaisuk Yoo),이귀영(Kwiyoung Lee) 한국자동차공학회 1997 한국자동차공학회 춘 추계 학술대회 논문집 Vol.1997 No.11_1
A model for predicting the residual gas fraction has been formulated in the present study. The model accounts for the contribution due to the backflow of exhaust gas from exhaust port to cylinder during valve overlap and trapped fraction of in-cylinder at intake valve open. This model is based on the physical flow process during valve overlap and a ideal cycle model of thermodynamic analysis.<br/> The model has been calibrated with in-cylinder hydrocarbon mesurements at different values of intake pressure, air-fuel ratio, and other experimental data. This model should be useful for predicting the residual gas fraction for cycle simulation analysis and engine heat release analysis.<br/>