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The Proactive Threat Protection Method from Predicting Resignation Throughout DRM Log Analysis and Monitor
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 Title & Authors
The Proactive Threat Protection Method from Predicting Resignation Throughout DRM Log Analysis and Monitor
Hyun, Miboon; Lee, Sangjin;
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 Abstract
Most companies are willing to spend money on security systems such as DRM, Mail filtering, DLP, USB blocking, etc., for data leakage prevention. However, in many cases, it is difficult that legal team take action for data case because usually the company recognized that after the employee had left. Therefore perceiving one`s resignation before the action and building up adequate response process are very important. Throughout analyzing DRM log which records every single file`s changes related with user`s behavior, the company can predict one`s resignation and prevent data leakage before those happen. This study suggests how to prevent for the damage from leaked confidential information throughout building the DRM monitoring process which can predict employee`s resignation.
 Keywords
DRM;Log Analysis;Outlier Detection;Prediction;Monitoring Process;Proactive Threat Detection;
 Language
Korean
 Cited by
 References
1.
Youngwoo Lee, "Trade secrets, keep it using the electronic fingerprint!," http://www.ytn.co.kr/_ln/0103_201009090008502441, YTN, 9, Aug. 2010.

2.
"Unfair Competition Prevention and Trade Secret Protection Act", http://www.law.go.kr/lsInfoP.do?lsiSeq=142374, The National Law Information Center, 30, Jul. 2013

3.
"Report on the Current Status of SMEs' Management of Industrial Confidential Information," Small & Medium Company Administration, pp.15, Jun. 2007.

4.
Christophe Leys and Christophe Ley, etc., "Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median," Journal of Experimental Social Psychology, pp. 1, Mar. 2013.

5.
Christophe Leys and Christophe Ley, etc., "Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median," Journal of Experimental Social Psychology, pp.2, Mar. 2013. crossref(new window)

6.
Manish B Dave, Mitesh B Nakrani, "Malicious User Detection in Spectrum Sensing for WRAN Using Different Outliers Techniques," IJETT, Vol.9, Mar. 2014.

7.
Songwon Seo, "A Review and Comparison of Methods for Detecting Outliers in Univariate Data Sets", University of Pittsburgh, pp. 47, 2006

8.
"Median absolute deviation," https://en.wikipedia.org/wiki/Median_absolute_deviation

9.
Francisco Augusto Alcaraz Gracia. "Tests to identify Outliers in Data Series," MATLAB 7.8, 18, Aug. 2010

10.
Sagar S. Sabade, Duncan M. Walker, "Evaluation of Effectiveness of Median of Absolute Deviations Outlier Rejectionbased IDDQ Testing for Burn-in Reduction",

11.
"A Comprehensive guide to Data Exploration," http://www.analyticsvidhya.com/blog/2015/02/outliers-detection-treatment-dataset/, Analytics Vidhya, 10, Jan. 2016