• Convergence Security Journal
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• v.15 no.6_2
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• pp.3-10
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• 2015

Recent massive data breaches or major security incidents show that threats posed by insiders have greatly increased over time. Especially, authorized insiders can cause more serious problems than external hackers can. Therefore there is a growing need to introduce a system that can monitor the insider threats in real time and prevent data breaches or security incidents in early-stage. In this paper, we propose a EITMS(Enterprise Insider-Threats Management System). EITMS detects the abnormal behaviors of authorized insiders based on the normal patterns made from their roles, duties and private activities. And, in order to prevent breaches and incidents in early-stage, a scoring system that can visualize the insider threats is also included.

• Journal of Information Science Theory and Practice
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• v.7 no.1
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• pp.52-71
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• 2019

An insider threat is a threat that comes from people within the organization being attacked. It can be described as a function of the motivation, opportunity, and capability of the insider. Compared to managing the dimensions of opportunity and capability, assessing one's motivation in committing malicious acts poses more challenges to organizations because it usually involves a more obtrusive process of psychological examination. The existing body of research in psycholinguistics suggests that automated text analysis of electronic communications can be an alternative for predicting and detecting insider threat through unobtrusive behavior monitoring. However, a major challenge in employing this approach is that it is difficult to minimize the risk of missing any potential threat while maintaining an acceptable false alarm rate. To deal with the trade-off between the risk of missed catches and the false alarm rate, we propose a unified psycholinguistic framework that consolidates multiple text analyzers to carry out sentiment analysis, emotion analysis, and topic modeling on electronic communications for unobtrusive psychological assessment. The user scenarios presented in this paper demonstrated how the trade-off issue can be attenuated with different text analyzers working collaboratively to provide more comprehensive summaries of users' psychological states.

• Management Science and Financial Engineering
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• v.19 no.1
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• pp.11-16
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• 2013

This paper derives an equilibrium asset price when there exist three kinds of traders in financial market: a risk-averse informed trader, noise traders, and risk neutral market makers. This paper is an extended version of Kyle's (1985, Econometrica) continuous time model by introducing insider's risk aversion. We obtain not only the equilibrium asset pricing and market depth parameter but also insider's value function and optimal insider's trading strategy explicitly. The comparative static shows that the market depth (the reciprocal of market pressure) increases with time and volatility of noise traders' trading.

• The Journal of the Korea institute of electronic communication sciences
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• v.3 no.4
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• pp.210-220
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• 2008

Insider threat is becoming a very serious issue in most organizations and management is responsible for security implementation. This study is to develop a personnel security management indicators in the areas of Personnel Assurance, Personnel Competence, and Security Environment and protection against insider threats. In this study, the information security management system and related papers are examined by reviewing the existing researches and cases. Proposed indicators are verified by pilot test, empirically analyzed to expose experts' perception and the validity, importance, and risk level of each indicators through a questionnaire. Result were encouraging, but additional study focused on personnel security management using factor analysis is needed in the future.

• Convergence Security Journal
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• v.15 no.5
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• pp.77-86
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• 2015

The organization shall minimize business risks associated with customer information leaks. Enhance information security activities through voluntary pre-check and must find a way to detect the personal information leakage caused by carelessness and neglect accident. Recently, many companies have introduced an information leakage prevention solution. However, there is a possibility of internal data leakage by the internal user who has permission to access the data. By this thread it is necessary to have the environment to analyze the habit and activity of the internal user. In this study, we use the SFI analytical technique that applies RFM model to evaluate the insider activity levels were carried out case studies is applied to the actual business.

• Journal of Advanced Navigation Technology
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• v.27 no.1
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• pp.16-22
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• 2023

Terrorists have been attacking in the vulnerable points of aviation sector with the diverse methods of attacks. Recently, Vulnerability is increasing because the Modus Operandi of Terrorism is carried out by exploitation of people in the form of employee working in aviation sector whose role provides them with privileged access to secured locations, secured items or security sensitive information. Furthermore, cases of insider threat are rising across the world with the phenomenon of personal radicalization through internet and social network service. The government of ROK must respond to insider threat could exploit to acts of unlawful interference and the security regulations should be established to prevent from insider threat in advance refer to the acts of unlawful interference carried out in foreign countries and the recommendations by USA, UK and ICAO.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.4
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• pp.125-139
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• 2017

In this paper, we developed a framework to detect and predict insider information leakage by collecting and restoring network traffic. For automated behavior analysis, many meta information and behavior information obtained using network traffic collection are used as machine learning features. By these features, we created and learned behavior model, network model and protocol-specific models. In addition, the ensemble model was developed by digitizing and summing the results of various models. We developed a function to present information leakage candidates and view meta information and behavior information from various perspectives using the visual analysis. This supports to rule-based threat detection and machine learning based threat detection. In the future, we plan to make an ensemble model that applies a regression model to the results of the models, and plan to develop a model with deep learning technology.

• Journal of Korea Multimedia Society
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• v.20 no.4
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• pp.614-628
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• 2017

Recently, Due to the rapid development of the internet and IT technology, users can conveniently use various services provided by the server anytime and anywhere. However, these technologies are exposed to various security threat such as tampering, eavesdropping, and exposing of user's identity and location information. In 2016, Nikooghadam et al. proposed a lightweight authentication and key agreement protocol preserving user anonymity. This paper overcomes the vulnerability of Nikooghadam's authentication protocol proposed recently. This paper suggests an enhanced remote user authentication protocol that protects user's password and provides perfect forward secrecy.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.2
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• pp.1-11
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• 2017

In this paper, we design and implement PADIL(Prediction And Detection of Information Leakage) system that predicts and detect information leakage behavior of insider by analyzing network traffic and applying a variety of machine learning methods. we defined the five-level information leakage model(Reconnaissance, Scanning, Access and Escalation, Exfiltration, Obfuscation) by referring to the cyber kill-chain model. In order to perform the machine learning for detecting information leakage, PADIL system extracts various features by analyzing the network traffic and extracts the behavioral features by comparing it with the personal profile information and extracts information leakage level features. We tested various machine learning methods and as a result, the DecisionTree algorithm showed excellent performance in information leakage detection and we showed that performance can be further improved by fine feature selection.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1722-1737
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• 2019

We developed an insider threat detection model to be used by organizations that repeat tasks at regular intervals. The model identifies the best combination of different feature selection algorithms, unsupervised learning algorithms, and standard scores. We derive a model specifically optimized for the organization by evaluating each combination in terms of accuracy, AUC (Area Under the Curve), and TPR (True Positive Rate). In order to validate this model, a four-year log was applied to the system handling sensitive information from public institutions. In the research target system, the user log was analyzed monthly based on the fact that the business process is processed at a cycle of one year, and the roles are determined for each person in charge. In order to classify the behavior of a user as abnormal, the standard scores of each organization were calculated and classified as abnormal when they exceeded certain thresholds. Using this method, we proposed an optimized model for the organization and verified it.