• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.923-926
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• 2012

안드로이드는 스마트폰에서 프로그램을 실행하도록 하는 구글에서 개발한 모바일 전용 운영체제로써 현재 수백만 대의 스마트폰, 태블릿PC에 탑재되어 있다. 안드로이드는 빠른 속도의 웹브라우저, 멀티 태스킹, 클라우드 컴퓨팅 기능, 다른 장치와 쉽게 연결하여서 공유하는 기능 등을 제공하고 있다. 이에 따라 많은 스마트폰 제품들이 안드로이드 운영체제를 탑재하여 출시하고 있으며, 안드로이드는 전 세계 스마트폰 운영체제 점유율의 절반가량을 차지하고 있다. 하지만 안드로이드 운영체제가 많이 사용됨에 따라 그에 따른 안드로이드 악성코드 또한 급격하게 증가하고 있다. 따라서 본 논문에서는 안드로이드 악성코드를 탐지 및 차단할 때 분석 비용을 감소시킬 수 있는 권한정보 관리를 통한 안드로이드 안티바이러스 어플리케이션을 제안한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.568-571
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• 2013

스마트 폰의 보급량이 증가함에 따라 모바일 악성코드의 위협도 높아졌다. 여러 스마트 폰 플랫폼 중 안드로이드 플랫폼은 높은 점유율과 개방형 플랫폼이라는 특성상 다른 플랫폼에 비해 악의적인 공격에 취약하다. 안드로이드 앱이 스마트 폰에 설치, 실행되기 위해서는 개발자의 서명이 요구된다. 안드로이드 서명체계는 다중 서명을 허용하는데, 다중서명 체계상 악용될 수 있는 취약점이 존재한다. 본 연구에서는 안드로이드 코드서명의 취약점을 이용하여 악성코드를 실행시키고 다른 앱을 감염시키는 악성 앱을 개발하여 취약점의 악용 가능성에 대해 연구하였다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.1
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• pp.61-68
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• 2015

Recently, with increasing use of smartphones, the security threats also have increased rapidly. Especially, the compromised smartphone is very dangerous because it could be exploited in a DDOS attacks such as cyberterrorism as well as in the leakage of personal information. However, most bot detection mechanisms are still unsuitable for smartphone with its lower computing capability and limited battery capacity because they incur additional computational overheads or require pre-defined signatures. In this paper, we present an efficient bot detection mechanism in smartphones. Our mechanism detects effectively bots in outgoing traffic by using a correlation between user events and network traffic. We have implemented its prototype in Android smartphone and measured its performance. The evaluation results show that our mechanism provides low overhead to detect bots in smartphones.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.1
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• pp.31-37
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• 2015

As the forms of cyberattacks become diverse, there has been reported another case of exploiting vulnerabilities revealed while processing either a document or multimedia file that was distributed for attacking purpose, which would replace the traditional method of distributing malwares directly. The attack is based upon the observation that the softwares such as document editer or multimedia player may reveal inherent vulnerabilities on some specific inputs. The fuzzing methods that provide invalid random inputs for test purpose could discover such exploits. This paper suggests a new fuzzing method on document applications that could work in mobile environments, in order to resolve the drawback that the existing methods run only in PC environments. Our methods could effectively discover the exploits of mobile applications, and thus could be utilized as a means of dealing with APT attacks in mobile environments.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.9
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• pp.1955-1964
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• 2011

With fast deployment of high-speed networks and various online services, the demand for massive content distribution is also growing fast. An approach that is increasingly visible in communication research community and in industry domain is peer-to-peer (P2P) networks. The P2P swarming technique enables a content distribution system to achieve higher throughput, avoid server or network overload, and be more resilient to failure and traffic fluctuation. Moreover, as a P2P-based architecture pushed the computing and bandwidth cost toward the network edge, it allows scalability to support a large number of subscribers on a global scale, while imposing little demand for equipment on the content providers. However, the P2P swarming burdens message exchange overheads on the system. In this paper, we propose a new protocol which provides confidentiality, authentication, integrity, and access control to P2P swarming. We implemented a prototype of our protocol on Android smart phone platform. We believe our approach can be straightforwardly adapted to existing commercial P2P content distribution systems with modest modifications to current implementations.

• Journal of the Korea Society for Simulation
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• v.29 no.2
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• pp.73-81
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• 2020

HCCL stands for Heterogenous Container Class Library. HCCL is a library that allows heterogeneous types of data to be stored in a container as a single record and to be constructed as a list of the records to be stored in database. With HCCL, encryption/decryption can be done based on the unified data type. Recently, IoT sensor which is embedded in smartphone enables developers to provide various convenient services to users. However, it is also true that infringement of personal information may occur in the process of transmitting sensor information to API and users need to be prepared for this situation in some sense. In this study, we developed a data model that enhances existing security using SEED cryptographic algorithms while managing information of sensors based on HCCL. Due to the fact that the Android environment does not provide permission management function for sensors, this study decided whether or not to encrypt sensor information based on the user's choice so that the user can determine the creation and storage of safe data. For verification of this work, we have presented the performance evaluation by comparing with the situation of storing the sensor data in plaintext.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.374-376
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• 2022

The Malware App Analysis Tool is a system that analyzes Android-based apps by the AI-based algorithm defined in the tool and detects whether malware code is included. Currently, as the spred of smartphones is activated, crimes using malware apps have increased, and accordingly, security for malware apps is required. Android operating systems used in smartphones have a share of more than 70% and are open-source-based, so not only will there be many vulnerabilities and malware, but also more damage to malware apps, increasing demand for tools to detect and analyze malware apps. However, this paper is proposed because there are many difficulties in designing and developing a malware app analysis tool because the security functional requirements for the malware app analysis tool are not clearly specified. Through the developed protection profile, technology can be improved based on the design and development of malware app analysis tools, safety can be secured by minimizing damage to malware apps, and furthermore, trust in malware app analysis tools can be guaranted through common criteria.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.8
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• pp.1551-1559
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• 2015

Recently, the performance of smart devices is almost similar to that of the existing PCs, thus the users of smart devices can perform similar works such as messengers, SNSs(Social Network Services), smart banking, etc. originally performed in PC environment using smart devices. Although the development of smart devices has led to positive impacts, it has caused negative changes such as an increase in security threat aimed at mobile environment. Specifically, the threats of mobile devices, such as leaking private information, generating unfair billing and performing DDoS(Distributed Denial of Service) attacks has continuously increased. Over 80% of the mobile devices use android platform, thus, the number of damage caused by mobile malware in android platform is also increasing. In this paper, we propose android based malware detection mechanism using time-series analysis, which is one of statistical-based detection methods.We use auto-regressive moving-average model which is extracting accurate predictive values based on existing data among time-series model. We also use fast and exact malware detection method by extracting possible malware data through Z-Score. We validate the proposed methods through the experiment results.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.11
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• pp.427-432
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• 2019

In this paper, we propose three approaches to modeling Android malware. The first method involves human security experts for meticulously selecting feature sets. With the second approach, we choose 300 features with the highest importance among the top 99% features in terms of occurrence rate. The third approach is to combine multiple models and identify malware through weighted voting. In addition, we applied a novel method of eliminating permission information which used to be regarded as a critical factor for distinguishing malware. With our carefully generated feature sets and the weighted voting by the ensemble algorithm, we were able to reach the highest malware detection accuracy of 97.8%. We also verified that discarding the permission information lead to the improvement in terms of false positive and false negative rates.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.4
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• pp.873-880
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• 2015

Since hardware-based kernel-integrity monitoring systems run in the environments that are isolated from the monitored OS, attackers in the monitored OS cannot undermine the security of monitoring systems. However, because the monitoring is performed by using physical addresses, the hardware-based monitoring systems are vulnerable to Address Translation Redirection Attack (ATRA) that manipulates virtual-to-physical memory translations. To ameliorate this problem, we propose a scheduler-based ATRA detection method. The method detects ATRA during the process scheduling by leveraging the fact that kernel scheduler engages every context switch of processes. We implemented a prototype on Android emulator and TizenTV, and verified that it successfully detected ATRA without incurring any significant performance loss.