• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.1
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• pp.145-154
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• 2014

In this paper, we propose a novel anti-malware system based on behavior profiling, called Andro-profiler. Andro-profiler consists of mobile devices and a remote server, and is implemented in Droidbox. Our aim is to detect and classify malware using an automatic classifier based on behavior profiling. First, we propose the representative behavior profiling for each malware family represented by system calls coupled with Droidbox system logs. This is done by executing the malicious application on an emulator and extracting integrated system logs. By comparing the behavior profiling of malicious applications with representative behavior profiling for each malware family, we can detect and classify them into malware families. Andro-profiler shows over 99% of classification accuracy in classifying malware families.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.3
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• pp.159-168
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• 2017

Smartphone malware has increased because Smartphone users has increased and smartphones are widely used in everyday life. Since 2012, Android has been the most mobile operating system. Owing to the open nature of Android, countless malware are in Android markets that seriously threaten Android security. Most of Android malware detection program does not detect malware to which bypass techniques apply and also does not detect unknown malware. In this paper, we propose lightweight method for detection of Android malware using static analysis and deep learning techniques. For experiments we crawl 7,000 apps from the Google Play Store and collect 6,120 malwares. The result show that proposed method can achieve 98.05% detection accuracy. Also, proposed method can detect about unknown malware families with good performance. On smartphones, the method requires 10 seconds for an analysis on average.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.6
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• pp.589-593
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• 2008

These days, as a side effect of the growth of the mobile devices, malwares for the mobile devices also tend to increase and become more dangerous. Because embedded Linux is one of the advanced OSes on mobile devices, a solution to preventing malwares from infecting and destroying embedded Linux will be needed. We present a scheme using signature verification for embedded Linux that prevents executallle-Infecting malwares. The proposed scheme works under collaboration between mobile devices and a server. Malware detection is delegated to the server. In a mobile device, only integrity of all executables and dynamic libraries is checked at kernel level every time by kernel modules using LSM hooks just prior to loading of executables and dynamic libraries. All procedures in the mobile devices are performed only at kernel level. In experiments with a mobile embedded device, we confirmed that the scheme is able to prevent all executable-Infecting malwares while minimizing damage caused by execution of malwares or infected files, power consumption and performance overheads caused by malware check routines.

• Convergence Security Journal
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• v.11 no.4
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• pp.67-75
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• 2011

Recently, the number of smartphone users is rising rapidly due to an influx of foreign smartphones and sales of domestic products. According to the increase of smartphone users, smartphone malwares are also increasing sharply. Hence it is necessary to protect smartphone against mobile malwares. There are device management protocols as SNMP, TR-069. But these protocols are not suitable for mobile device management because of restrictive management function and unsupported mobility. OMA DM which is a standard for mobile device management has been adopted as mobile device management protocol for most of 2G,3G. Thus it amounts that OMA DM is suitable for smartphone management system. In this paper, the mobile device management system based on OMA DM is designed. This system can remove smartphone malware by remote control.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.01a
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• pp.153-154
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• 2018

전 세계 모바일 게임 소비 시장의 증가와 사용자들이 지속적으로 증가하는 반면 랜섬웨어와 같은 악성 프로그램들이 악의적인 목적을 위하여 모바일게임 시장에 피해를 주는 사례들도 지속적으로 증가하는 것도 사실이다. 본 논문에서는 모바일 게임을 이용한 악성코드 위협으로부터 보호하기 위하여 4차 산업의 가장 핵심 기술인 인공지능의 학습기술에 악성코드 분석기술을 연계시켜 새로운 모바일 악성코드 탐지와 속도를 향상시키는 기술의 필요성을 제시한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.454-475
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• 2018

This paper presents a novel Android malware classification model planned to classify and categorize Android malicious code at Drebin dataset. The amount of malicious mobile application targeting Android based smartphones has increased rapidly. In this paper, Restricted Boltzmann Machine and Deep Belief Network are used to classify malware into families of Android application. A texture-fingerprint based approach is proposed to extract or detect the feature of malware content. A malware has a unique "image texture" in feature spatial relations. The method uses information on texture image extracted from malicious or benign code, which are mapped to uncompressed gray-scale according to the texture image-based approach. By studying and extracting the implicit features of the API call from a large number of training samples, we get the original dynamic activity features sets. In order to improve the accuracy of classification algorithm on the features selection, on the basis of which, it combines the implicit features of the texture image and API call in malicious code, to train Restricted Boltzmann Machine and Back Propagation. In an evaluation with different malware and benign samples, the experimental results suggest that the usability of this method---using Deep Belief Network to classify Android malware by their texture images and API calls, it detects more than 94% of the malware with few false alarms. Which is higher than shallow machine learning algorithm clearly.

• Convergence Security Journal
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• v.20 no.3
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• pp.53-60
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• 2020

At the present stage of the fourth industrial revolution, machine learning and artificial intelligence technologies are rapidly developing, and there is a movement to apply machine learning technology in the security field. Malicious code, including new and transformed, generates an average of 390,000 a day worldwide. Statistics show that security companies ignore or miss 31 percent of alarms. As many malicious codes are generated, it is becoming difficult for humans to detect all malicious codes. As a result, research on the detection of malware and network intrusion events through machine learning is being actively conducted in academia and industry. In international conferences and journals, research on security data analysis using deep learning, a field of machine learning, is presented. have. However, these papers focus on detection accuracy and modify several parameters to improve detection accuracy but do not consider the ratio of dataset. Therefore, this paper aims to reduce the cost and resources of many machine learning research by finding the ratio of dataset that can derive the highest detection accuracy in CNN Mobile net-based malware detection model.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.4B
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• pp.599-609
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• 2010

Korean government has recently abrogated WIPI policy to open domestic mobile phone market to the world, which may result in the influx of foreign smart phones. This circumstance has given users more wide range of choices to buy a product and also has brought benefit to buy mobile phone cheaply. On the other hands, this change might have brought potential danger of mobile malware incidents which have only occurred in foreign countries. There are standardized analysis methods and response guides for computer malwares, not but for mobile malwares in our country. In this paper, we introduce existing mobile malwares and available tools for their analysis. Considering domestic circumstances which might not be properly protected against mobile malwares, we propose analysis methods and response guide of mobile malwares.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.17-22
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• 2021

The attack trend on end-users via mobile devices is increasing in both the danger level and the number of attacks. Especially, mobile devices using the Android operating system are being recognized as increasingly being exploited and attacked strongly. In addition, one of the recent attack methods on the Android operating system is to take advantage of Android Package Kit (APK) files. Therefore, the problem of early detecting and warning attacks on mobile devices using the Android operating system through the APK file is very necessary today. This paper proposes to use the method of analyzing abnormal behavior of APK files and use it as a basis to conclude about signs of malware attacking the Android operating system. In order to achieve this purpose, we propose 2 main tasks: i) analyzing and extracting abnormal behavior of APK files; ii) detecting malware in APK files based on behavior analysis techniques using machine learning or deep learning algorithms. The difference between our research and other related studies is that instead of focusing on analyzing and extracting typical features of APK files, we will try to analyze and enumerate all the features of the APK file as the basis for classifying malicious APK files and clean APK files.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.389-396
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• 2022

Smartphones are growing more susceptible as technology develops because they contain sensitive data that offers a severe security risk if it falls into the wrong hands. The Android OS includes permissions as a crucial component for safeguarding user privacy and confidentiality. On the other hand, mobile malware continues to struggle with permission misuse. Although permission-based detection is frequently utilized, the significant false alarm rates brought on by the permission-based issue are thought to make it inadequate. The present detection method has a high incidence of false alarms, which reduces its ability to identify permission-based attacks. By using permission features with intent, this research attempted to improve permission-based detection. However, it creates an excessive number of features and increases the likelihood of false alarms. In order to generate the optimal number of features created and boost the quality of features chosen, this research developed an intersection feature approach. Performance was assessed using metrics including accuracy, TPR, TNR, and FPR. The most important characteristics were chosen using the Correlation Feature Selection, and the malicious program was categorized using SVM and naive Bayes. The Intersection Feature Technique, according to the findings, reduces characteristics from 486 to 17, has a 97 percent accuracy rate, and produces 0.1 percent false alarms.