• 디지털융복합연구
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• 제18권12호
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• pp.217-229
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• 2020

세계적으로, 정보보호는 조직의 필수적인 관리 조건이 되고 있으며, 조직들은 정보보안을 위하여 높은 수준의 자원을 지속적으로 투자하고 있다. 조직 내부자들의 보안 위협은 감소하지 않고 있어, 정보보안 행동 준수를 위한 관심이 필요한 상황이다. 본 연구의 목적은 조직원들의 보안 회피 행동의 원인인 역할갈등을 완화시키기 위한 선행 요인을 제시하는 것이다. 연구는 정보보안 정책을 보유한 조직에서 근무하는 조직원을 대상으로 설문을 실시하였으며, 383개의 표본을 활용하여 구조방정식모델링을 통한 가설 검증을 하였다. 가설 검증 결과, 역할갈등이 회피행동을 증가시키는 것으로 나타났으며, 목표 난이도와 세밀성, 공정성, 신뢰가 역할갈등을 완화하는 것으로 나타났다. 특히, 공정성은 신뢰를 통해 역할갈등과 회피 행동 감소에 영향을 주는 것으로 나타났다. 연구 결과는 조직원의 정보보안 회피행동 원인과 완화 요인을 제시함으로써, 정보보안 수준 향상을 위한 정보보안 전략 수립에 영향을 줄 것으로 판단한다.

• Asia pacific journal of information systems
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• 제22권1호
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• pp.53-77
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• 2012

Financial firms, especially large scaled firms such as KB bank, NH bank, Samsung Card, Hana SK Card, Hyundai Capital, Shinhan Card, etc. should be securely dealing with the personal financial information. Indeed, people have tended to believe that those big financial companies are relatively safer in terms of information security than typical small and medium sized firms in other industries. However, the recent incidents of personal information privacy invasion showed that this may not be true. Financial firms have increased the investment of information protection and security, and they are trying to prevent the information privacy invasion accidents by doing all the necessary efforts. This paper studies how effectively a financial firm will be able to avoid personal financial information privacy invasion that may be deliberately caused by internal staffs. Although there are several literatures relating to information security, to our knowledge, this is the first study to focus on the behavior of internal staffs. The big financial firms are doing variety of information security activities to protect personal information. This study is to confirm what types of such activities actually work well. The primary research model of this paper is based on Theory of Planned Behavior (TPB) that describes the rational choice of human behavior. Also, a variety of activities to protect the personal information of financial firms, especially credit card companies with the most customer information, were modeled by the four-step process Security Action Cycle (SAC) that Straub and Welke (1998) claimed. Through this proposed conceptual research model, we study whether information security activities of each step could suppress personal information abuse. Also, by measuring the morality of internal staffs, we checked whether the act of information privacy invasion caused by internal staff is in fact a serious criminal behavior or just a kind of unethical behavior. In addition, we also checked whether there was the cognition difference of the moral level between internal staffs and the customers. Research subjects were customer call center operators in one of the big credit card company. We have used multiple regression analysis. Our results showed that the punishment of the remedy activities, among the firm's information security activities, had the most obvious effects of preventing the information abuse (or privacy invasion) by internal staff. Somewhat effective tools were the prevention activities that limited the physical accessibility of non-authorities to the system of customers' personal information database. Some examples of the prevention activities are to make the procedure of access rights complex and to enhance security instrument. We also found that 'the unnecessary information searches out of work' as the behavior of information abuse occurred frequently by internal staffs. They perceived these behaviors somewhat minor criminal or just unethical action rather than a serious criminal behavior. Also, there existed the big cognition difference of the moral level between internal staffs and the public (customers). Based on the findings of our research, we should expect that this paper help practically to prevent privacy invasion and to protect personal information properly by raising the effectiveness of information security activities of finance firms. Also, we expect that our suggestions can be utilized to effectively improve personnel management and to cope with internal security threats in the overall information security management system.

• International Journal of Computer Science & Network Security
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• 제21권11호
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• pp.165-170
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• 2021

Discrete event systems interact with the external environment to decide which action plan is adequate. Some of these interactions are not predictable in the modelling phase and require consequently an adaptation of the system to the metamorphosed behavior of the environment. One of the challenging issues is to guarantee safety behavior when failures tend to derive the system from normal status. In this paper we propose a framework to combine diagnose technique with adaptive control to avoid unsafe sate an maintain the normal behavior as long as possible.

• 정보보호학회논문지
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• 제24권1호
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• pp.135-144
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• 2014

디지털 포렌식 조사를 회피하기 위한 안티포렌식이 발전하고 있는 가운데, 안티포렌식 행위를 찾아내기 위한 포렌식 방법들 또한 다각도로 연구되고 있다. 사용자 행위분석을 위한 여러 요소 중 응용프로그램의 아이콘 정보를 저장하고 있는 IconCache.db 파일은 디지털 포렌식 조사를 위한 의미 있는 정보들을 제공하고 있다. 본 논문은 IconCache.db 파일의 특성을 알아보고 안티포렌식에 대응할 수 있는 활용방안을 제시한다.

• International Journal of Computer Science & Network Security
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• 제21권6호
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• pp.245-251
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• 2021

This work aims to focus on the current features and characteristics of Human Element and Artificial intelligence (AI), ask some questions about future information security, and whether we can avoid human errors by improving machine learning and AI or invest in human knowledge more and work them both together in the best way possible? This work represents several related research results on human behavior towards information security, specified with elements and factors like knowledge and attitude, and how much are they invested for ISA (information security awareness), then presenting some of the latest studies on AI and their contributions to further improvements, making the field more securely advanced, we aim to open a new type of thinking in the cybersecurity field and we wish our suggestions of utilizing each point of strengths in both human attributions in software security and the existence of a well-built AI are going to make better future software security.

• Journal of Communications and Networks
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• 제15권2호
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• pp.122-131
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• 2013

We address the problem of effective vehicular routing in hostile scenarios where malicious nodes intend to jeopardize the delivery of messages. Compromised vehicles can severely affect the performance of the network by a number of attacks, such as selectively dropping messages, manipulating them on the fly, and the likes. One of the best performing solutions that has been used in static wireless sensor networks to deal with these attacks is based on the concept of watchdog nodes (also known as guard nodes) that collaborate to continue the forwarding of data packets in case a malicious behavior in a neighbor node is detected. In this work, we consider the beacon-less routing algorithm for vehicular environments routing protocol, which has been previously shown to perform very well in vehicular networks, and analyze whether a similar solution would be feasible for vehicular environments. Our simulation results in an urban scenario show that watchdog nodes are able to avoid up to a 50% of packet drops across different network densities and for different number of attackers, without introducing a significant increase in terms of control overhead. However, the overall performance of the routing protocol is still far from optimal. Thus, in the case of vehicular networks, watchdog nodes alone are not able to completely alleviate these security threats.

• International Journal of Computer Science & Network Security
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• 제23권5호
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• pp.163-171
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• 2023

The intelligent transportation system has made a huge leap in the level of human services, which has had a positive impact on the quality of life of users. On the other hand, these services are becoming a new source of risk due to the use of data collected from vehicles, on which intelligent systems rely to create automatic contextual adaptation. Most of the popular privacy protection methods, such as Dummy and obfuscation, cannot be used with many services because of their impact on the accuracy of the service provided itself, they depend on changing the number of vehicles or their physical locations. This research presents a new approach based on the shuffling Nicknames of vehicles. It fully maintains the quality of the service and prevents tracking users permanently, penetrating their privacy, revealing their whereabouts, or discovering additional details about the nature of their behavior and movements. Our approach is based on creating a central Nicknames Pool in the cloud as well as distributed subpools in fog nodes to avoid intelligent delays and overloading of the central architecture. Finally, we will prove by simulation and discussion by examples the superiority of the proposed approach and its ability to adapt to new services and provide an effective level of protection. In the comparison, we will rely on the wellknown privacy criteria: Entropy, Ubiquity, and Performance.

• 정보보호학회논문지
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• 제29권1호
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• pp.45-55
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• 2019

DEX 파일과, SO 파일로 알려진 공유 라이브러리 파일은 안드로이드 어플리케이션의 행위를 결정짓는 중요한 구성요소이다. DEX 파일은 Java 코드로 구현된 실행파일이며, SO 파일은 ELF 파일 형식을 따르며 C/C++와 같은 네이티브 코드로 구현된다. Java 영역과 네이티브 코드 영역은 런타임에 상호작용할 수 있다. 오늘날 안드로이드 악성코드는 지속적으로 증가하고 있으며, 악성코드로 탐지되는 것을 회피하기 위한 다양한 우회 기법을 적용한다. 악성코드 분석을 회피하기 위하여 분석이 어려운 네이티브 코드에서 악성 행위를 수행하는 어플리케이션 또한 존재한다. 기존 연구는 Java 코드와 네이티브 코드를 모두 포함하는 함수 호출 관계를 표시하지 못하거나, 여러 개의 DEX을 포함하는 어플리케이션을 분석하지 못하는 문제점을 지닌다. 본 논문에서는 안드로이드 어플리케이션의 DEX 파일과 SO 파일을 분석하여 Java 코드 및 네이티브 코드에서 호출 관계를 추출하는 시스템을 설계 및 구현한다.

• International Journal of Computer Science & Network Security
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• 제24권5호
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• pp.111-118
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• 2024

In general network-based intrusion detection system is designed to detect malicious behavior directed at a network or its resources. The key goal of this paper is to look at network data and identify whether it is normal traffic data or anomaly traffic data specifically for accounting information systems. In today's world, there are a variety of principles for detecting various forms of network-based intrusion. In this paper, we are using supervised machine learning techniques. Classification models are used to train and validate data. Using these algorithms we are training the system using a training dataset then we use this trained system to detect intrusion from the testing dataset. In our proposed method, we will detect whether the network data is normal or an anomaly. Using this method we can avoid unauthorized activity on the network and systems under that network. The Decision Tree and K-Nearest Neighbor are applied to the proposed model to classify abnormal to normal behaviors of network traffic data. In addition to that, Logistic Regression Classifier and Support Vector Classification algorithms are used in our model to support proposed concepts. Furthermore, a feature selection method is used to collect valuable information from the dataset to enhance the efficiency of the proposed approach. Random Forest machine learning algorithm is used, which assists the system to identify crucial aspects and focus on them rather than all the features them. The experimental findings revealed that the suggested method for network intrusion detection has a neglected false alarm rate, with the accuracy of the result expected to be between 95% and 100%. As a result of the high precision rate, this concept can be used to detect network data intrusion and prevent vulnerabilities on the network.

• 한국컴퓨터정보학회논문지
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• 제25권11호
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• pp.131-138
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• 2020

멀웨어는 일반적으로 다른 사용자의 컴퓨터시스템에 침입하여 개발자가 의도하는 악의적인 행위를 일으키는 컴퓨터프로그램으로 인식되지만 사이버 공간에서는 적대국을 공격하기 위한 사이버 무기로써 사용되기도 한다. 사이버 무기로서 멀웨어가 갖춰야 할 가장 중요한 요소는 상대방의 탐지시스템에 의해 탐지되기 이전에 의도한 목적을 달성하여야 한다는 것인데, 하나의 멀웨어를 상대방의 탐지 시스템을 피하도록 제작하는 데에는 많은 시간과 전문성이 요구된다. 우리는 DCM 기법을 사용하여, 바이너리코드 형태의 멀웨어를 입력하면 변종 멀웨어를 자동으로 생성해 주는 프레임워크를 제안한다. 이 프레임워크 안에서 샘플 멀웨어가 자동으로 변종 멀웨어로 변환되도록 구현하였고, 시그니쳐 기반의 멀웨어 탐지시스템에서는 이 변종 멀웨어가 탐지되지 않는 것을 확인하였다.