http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
( Youngin You ),( Junhyoung Oh ),( Sooheon Kim ),( Kyungho Lee ) 한국인터넷정보학회 2018 KSII Transactions on Internet and Information Syst Vol.12 No.10
As the area covered by the CPS grows wider, agencies such as public institutions and critical infrastructure are collectively measuring and evaluating information security capabilities. Currently, these methods of measuring information security are a concrete method of recommendation in related standards. However, the security controls used in these methods are lacking in connectivity, causing silo effect. In order to solve this problem, there has been an attempt to study the information security management system in terms of maturity. However, to the best of our knowledge, no research has considered the specific definitions of each level that measures organizational security maturity or specific methods and criteria for constructing such levels. This study developed an information security maturity model that can measure and manage the information security capability of critical infrastructure based on information provided by an expert critical infrastructure information protection group. The proposed model is simulated using the thermal power sector in critical infrastructure of the Republic of Korea to confirm the possibility of its application to the field and derive core security processes and goals that constitute infrastructure security maturity. The findings will be useful for future research or practical application of infrastructure ISMSs.
Detecting Potential Insider Threat: Analyzing Insiders’ Sentiment Exposed in Social Media
Park, Won,You, Youngin,Lee, Kyungho Hindawi Limited 2018 Security and communication networks Vol.2018 No.-
<P>In the era of Internet of Things (IoT), impact of social media is increasing gradually. With the huge progress in the IoT device, insider threat is becoming much more dangerous. Trying to find what kind of people are in high risk for the organization, about one million of tweets were analyzed by sentiment analysis methodology. Dataset made by the web service “Sentiment140” was used to find possible malicious insider. Based on the analysis of the sentiment level, users with negative sentiments were classified by the criteria and then selected as possible malicious insiders according to the threat level. Machine learning algorithms in the open-sourced machine learning software “Weka (Waikato Environment for Knowledge Analysis)” were used to find the possible malicious insider. Decision Tree had the highest accuracy among supervised learning algorithms and K-Means had the highest accuracy among unsupervised learning. In addition, we extract the frequently used words from the topic modeling technique and then verified the analysis results by matching them to the information security compliance elements. These findings can contribute to achieve higher detection accuracy by combining individual’s characteristics to the previous studies such as analyzing system behavior.</P>