• Title/Summary/Keyword: Mobile Malware

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Cloud based Android Mobile Malware Detection Using Stage by Stage Analysis (단계적 분석 기법을 이용한 클라우드 기반 모바일 악성코드 탐지)

  • Lee, Jina;Min, Jae-Won;Jung, Sung-Min;Chung, Tai-Myoung
    • Proceedings of the Korea Information Processing Society Conference
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    • 2012.11a
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    • pp.1076-1079
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    • 2012
  • 스마트폰의 사용이 생활에 필수적인 요소가 되었다. 스마트폰 특징의 가장 핵심적인 부분이 다양한 콘텐츠를 사용자의 취향에 맞게 선택 할 수 있다는 점이기에 스마트폰의 콘텐츠 시장 또한 빠르게 커지고 있다. 오픈 마켓인 안드로이드의 특성 상 누구나 어플리케이션을 만들어 원하는 곳에 배포할 수 있고 어플리케이션을 다운받을 수 있는 소스도 한정되어 있지 않기 때문에 스마트폰 보안을 위협하는 악의적인 어플리케이션에 노출되기 쉽다. 개인적인 정보가 저장되어 있는 핸드폰의 특징 상 악성코드에 노출 될 경우 전화번호부 유출로 인한 인한 스팸이나 피싱에서 크게는 금융정보 유출까지, 입을 수 있는 피해가 크다. 이를 방지하기 위해 클라우드 컴퓨팅을 이용해 단계적으로 악의적인 어플리케이션을 걸러 내고 클라우드 서버에 어플리케이션 실행 환경을 제공함으로써 사용자의 기기를 안전하게 보호 할 수 있는 시스템을 제안한다.

Normal and Malicious Application Pattern Analysis using System Call Event on Android Mobile Devices for Similarity Extraction (안드로이드 모바일 정상 및 악성 앱 시스템 콜 이벤트 패턴 분석을 통한 유사도 추출 기법)

  • Ham, You Joung;Lee, Hyung-Woo
    • Journal of Internet Computing and Services
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    • v.14 no.6
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    • pp.125-139
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    • 2013
  • Distribution of malicious applications developed by attackers is increasing along with general normal applications due to the openness of the Android-based open market. Mechanism that allows more accurate ways to distinguish normal apps and malicious apps for common mobile devices should be developed in order to reduce the damage caused by the rampant malicious applications. This paper analysed the normal event pattern from the most highly used game apps in the Android open market to analyse the event pattern from normal apps and malicious apps of mobile devices that are based on the Android platform, and analysed the malicious event pattern from the malicious apps and the disguising malicious apps in the form of a game app among 1260 malware samples distributed by Android MalGenome Project. As described, experiment that extracts normal app and malicious app events was performed using Strace, the Linux-based system call extraction tool, targeting normal apps and malicious apps on Android-based mobile devices. Relevance analysis for each event set was performed on collected events that occurred when normal apps and malicious apps were running. This paper successfully extracted event similarity through this process of analyzing the event occurrence characteristics, pattern and distribution on each set of normal apps and malicious apps, and lastly suggested a mechanism that determines whether any given app is malicious.

AI Security Plan for Public Safety Network App Store (재난안전통신망 앱스토어를 위한 AI 보안 방안 마련)

  • Jung, Jae-eun;Ahn, Jung-hyun;Baik, Nam-kyun
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2021.10a
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    • pp.458-460
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    • 2021
  • The provision and application of public safety network in Korea is still insufficient for security response to the mobile app of public safety network in the stages of development, initial construction, demonstration, and initial service. The available terminals on the Disaster Safety Network (PS-LTE) are open, Android-based, dedicated terminals that potentially have vulnerabilities that can be used for a variety of mobile malware, requiring preemptive responses similar to FirstNet Certified in U.S and Google's Google Play Protect. In this paper, before listing the application service app on the public safety network mobile app store, we construct a data set for malicious and normal apps, extract features, select the most effective AI model, perform static and dynamic analysis, and analyze Based on the result, if it is not a malicious app, it is suggested to list it in the App Store. As it becomes essential to provide a service that blocks malicious behavior app listing in advance, it is essential to provide authorized authentication to minimize the security blind spot of the public safety network, and to provide certified apps for disaster safety and application service support. The safety of the public safety network can be secured.

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Efficient File System Level Encryption Mechanism Using HSM (HSM을 이용한 효율적인 파일시스템 암호화 메커니즘)

  • Kang, Cheol-Oh;Won, Jong-Jin;Park, Sung-Jin;Ryou, Jea-Cheol
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.23 no.5
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    • pp.849-858
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    • 2013
  • In today's mobile computing environment, there are many threats, such as device loss or theft, malware, to the sensitive information stored on end user device. To prevent disclosure of information, encryption and authentication method are properly adjusted to the device. In cryptographic file systems, CBC mode of operation has been commonly used. It requires an IV need not be secret, but must be unpredictable and protect integrity of the IV. In this paper, we propose file system-level encryption mechanism with HSM that satisfy the requirement of the IV and improve the performance. Moreover, Design and experimental results prove the efficiency of our proposed method.

Optimal Machine Learning Model for Detecting Normal and Malicious Android Apps (안드로이드 정상 및 악성 앱 판별을 위한 최적합 머신러닝 기법)

  • Lee, Hyung-Woo;Lee, HanSeong
    • Journal of Internet of Things and Convergence
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    • v.6 no.2
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    • pp.1-10
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    • 2020
  • The mobile application based on the Android platform is simple to decompile, making it possible to create malicious applications similar to normal ones, and can easily distribute the created malicious apps through the Android third party app store. In this case, the Android malicious application in the smartphone causes several problems such as leakage of personal information in the device, transmission of premium SMS, and leakage of location information and call records. Therefore, it is necessary to select a optimal model that provides the best performance among the machine learning techniques that have published recently, and provide a technique to automatically identify malicious Android apps. Therefore, in this paper, after adopting the feature engineering to Android apps on official test set, a total of four performance evaluation experiments were conducted to select the machine learning model that provides the optimal performance for Android malicious app detection.

The blocking method for accessing toward malicious sites based on Android platform (안드로이드 플랫폼 기반 악성사이트 차단 방법)

  • Kim, Dae-Cheong;Ryou, Jae-Cheol
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.24 no.3
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    • pp.499-505
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    • 2014
  • According to the increasing use of smart devices such as smart phones and tablets, the service that targets mobile office, finance and e-government for convenience of usage and productivity has emerged significantly. As a result, important information is treated with the smart devices and also, the malicious activity that targets smart devices is increasing steadily. In particular, the damage case by harmful sites, malware distribution sites and phishing sites that targets smart devices has occurred steadily and it has emerged as a social issue. In the case of smart devices, the Android platform is occupied the 90% in Korea, 2013 therefore the method of device block level is required to resolve the social issues of smart devices. In this paper, we propose a method that can be effectively blocked when you try to access an illegal site to Web browser on the Android platform and develop the application and also analyze the wrong site block function.

A Scheme for Identifying Malicious Applications Based on API Characteristics (API 특성 정보기반 악성 애플리케이션 식별 기법)

  • Cho, Taejoo;Kim, Hyunki;Lee, Junghwan;Jung, Moongyu;Yi, Jeong Hyun
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.26 no.1
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    • pp.187-196
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    • 2016
  • Android applications are inherently vulnerable to a repackaging attack such that malicious codes are easily inserted into an application and then resigned by the attacker. These days, it occurs often that such private or individual information is leaked. In principle, all Android applications are composed of user defined methods and APIs. As well as accessing to resources on platform, APIs play a role as a practical functional feature, and user defined methods play a role as a feature by using APIs. In this paper we propose a scheme to analyze sensitive APIs mostly used in malicious applications in terms of how malicious applications operate and which API they use. Based on the characteristics of target APIs, we accumulate the knowledge on such APIs using a machine learning scheme based on Naive Bayes algorithm. Resulting from the learned results, we are able to provide fine-grained numeric score on the degree of vulnerabilities of mobile applications. In doing so, we expect the proposed scheme will help mobile application developers identify the security level of applications in advance.

Secure Management Method for Private Key using Smartphon's Information (스마트폰 고유정보를 이용한 안전한 개인키 관리 방안)

  • Kim, Seon-Joo
    • The Journal of the Korea Contents Association
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    • v.16 no.8
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    • pp.90-96
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    • 2016
  • The 3390 million people, around 83% of the adult population in Korea use smartphone. Although the safety problem of the certificate has been occurred continuously, most of these users use the certificate. These safety issues as a solution to 'The owner of a mobile phone using SMS authentication technology', 'Biometric authentication', etc are being proposed. but, a secure and reliable authentication scheme has not been proposed for replace the certificate yet. and there are many attacks to steal the certificate and private key. For these reasons, security experts recommend to store the certificate and private key on usb flash drive, security tokens, smartphone. but smartphones are easily infected malware, an attacker can steal certificate and private key by malicious code. If an attacker snatchs the certificate, the private key file, and the password for the private key password, he can always act as valid user. In this paper, we proposed a safe way to keep the private key on smartphone using smartphone's unique information and user password. If an attacker knows the user password, the certificate and the private key, he can not know the smart phone's unique information, so it is impossible to use the encrypted private key. Therefore smartphone user use IT service safely.

Development of Software-Defined Perimeter-based Access Control System for Security of Cloud and IoT System (Cloud 및 IoT 시스템의 보안을 위한 소프트웨어 정의 경계기반의 접근제어시스템 개발)

  • Park, Seung-Kyu
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.21 no.2
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    • pp.15-26
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    • 2021
  • Recently, as the introduction of cloud, mobile, and IoT has become active, there is a growing need for technology development that can supplement the limitations of traditional security solutions based on fixed perimeters such as firewalls and Network Access Control (NAC). In response to this, SDP (Software Defined Perimeter) has recently emerged as a new base technology. Unlike existing security technologies, SDP can sets security boundaries (install Gateway S/W) regardless of the location of the protected resources (servers, IoT gateways, etc.) and neutralize most of the network-based hacking attacks that are becoming increasingly sofiscated. In particular, SDP is regarded as a security technology suitable for the cloud and IoT fields. In this study, a new access control system was proposed by combining SDP and hash tree-based large-scale data high-speed signature technology. Through the process authentication function using large-scale data high-speed signature technology, it prevents the threat of unknown malware intruding into the endpoint in advance, and implements a kernel-level security technology that makes it impossible for user-level attacks during the backup and recovery of major data. As a result, endpoint security, which is a weak part of SDP, has been strengthened. The proposed system was developed as a prototype, and the performance test was completed through a test of an authorized testing agency (TTA V&V Test). The SDP-based access control solution is a technology with high potential that can be used in smart car security.

Suggestion of Selecting features and learning models for Android-based App Malware Detection (안드로이드 기반 앱 악성코드 탐지를 위한 Feature 선정 및 학습모델 제안)

  • Bae, Se-jin;Rhee, Jung-soo;Baik, Nam-kyun
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2022.05a
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    • pp.377-380
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    • 2022
  • An application called an app can be downloaded and used on mobile devices. Among them, Android-based apps have the disadvantage of being implemented on an open source basis and can be exploited by anyone, but unlike iOS, which discloses only a small part of the source code, Android is implemented as an open source, so it can analyze the code. However, since anyone can participate in changing the source code of open source-based Android apps, the number of malicious codes increases and types are bound to vary. Malicious codes that increase exponentially in a short period of time are difficult for humans to detect one by one, so it is efficient to use a technique to detect malicious codes using AI. Most of the existing malicious app detection methods are to extract Features and detect malicious apps. Therefore, three ways to select the optimal feature to be used for learning after feature extraction are proposed. Finally, in the step of modeling with optimal features, ensemble techniques are used in addition to a single model. Ensemble techniques have already shown results beyond the performance of a single model, as has been shown in several studies. Therefore, this paper presents a plan to select the optimal feature and implement a learning model for Android app-based malicious code detection.

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