• Journal of Internet Computing and Services
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• v.19 no.5
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• pp.67-75
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• 2018

According to Symantec's Internet Security Threat Report(2018), Internet security threats such as Cryptojackings, Ransomwares, and Mobile malwares are rapidly increasing and diversifying. It means that detection of malwares requires not only the detection accuracy but also versatility. In the past, malware detection technology focused on qualitative performance due to the problems such as encryption and obfuscation. However, nowadays, considering the diversity of malware, versatility is required in detecting various malwares. Additionally the optimization is required in terms of computing power for detecting malware. In this paper, we present Stream Order(SO)-CNN and Incremental Coordinate(IC)-CNN, which are malware detection schemes using CNN(Convolutional Neural Network) that effectively detect intelligent and diversified malwares. The proposed methods visualize each malware binary file onto a fixed sized image. The visualized malware binaries are learned through GoogLeNet to form a deep learning model. Our model detects and classifies malwares. The proposed method reveals better performance than the conventional method.

• The Journal of the Korea Contents Association
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• v.18 no.4
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• pp.305-313
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• 2018

With the explosive growth of smart phones and efficiency, the Android of an open mobile operating system is gradually increasing in the use and the availability. Android systems has proven its availability and stability in the mobile devices, the home appliances's operating systems, the IoT products, and the mechatronics. However, as the usability increases, the malicious code based on Android also increases exponentially. Unlike ordinary PCs, if malicious codes are infiltrated into mobile products, mobile devices can not be used as a lock and can be leaked a large number of personal contacts, and can be lead to unnecessary billing, and can be cause a huge loss of financial services. Therefore, we proposed a method to detect and delete malicious files in real time in order to solve this problem. In this paper, we also designed a method to detect and delete malicious codes in a more effective manner through the process of installing Android-based applications and signature-based malicious code detection method. The method we proposed and designed can effectively detect malicious code in a limited resource environment, such as mobile environments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2188-2203
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• 2021

With the rapid development of mobile Internet, smart phones have been widely popularized, among which Android platform dominates. Due to it is open source, malware on the Android platform is rampant. In order to improve the efficiency of malware detection, this paper proposes deep learning Android malicious detection system based on behavior features. First of all, the detection system adopts the static analysis method to extract different types of behavior features from Android applications, and extract sensitive behavior features through Term frequency-inverse Document Frequency algorithm for each extracted behavior feature to construct detection features through unified abstract expression. Secondly, Long Short-Term Memory neural network model is established to select and learn from the extracted attributes and the learned attributes are used to detect Android malicious applications, Analysis and further optimization of the application behavior parameters, so as to build a deep learning Android malicious detection method based on feature analysis. We use different types of features to evaluate our method and compare it with various machine learning-based methods. Study shows that it outperforms most existing machine learning based approaches and detects 95.31% of the malware.

• Journal of Korea Society of Industrial Information Systems
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• v.20 no.5
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• pp.37-44
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• 2015

An antivirus is a widely used solution for detecting malicious softwares in client devices. The performance of antivirus solutions in the mobile client environment is critical due to its resource constrains. Many solutions light-weighting client's overhead in the mobile client environment have been developed. However, most solutions require platform modifications or software installations and it decreases their realizations in practice. In this paper, we propose a solution detecting malwares on networks using the Software Defined Network (SDN). Our main goal is designing a solution detecting malwares of mobile client without involving the client into the work. We contribute to provide a solution that does not require client-side installations or modifications and so is easily applicable in practice.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.1-7
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• 2015

As recent smartphone is used around the world, all of the subscribers of the mobile communication is up to 47.7% about 24 million people. Smartphone has a vulnerability to security, and security-related incidents are increased in damage with the smartphone. However, precautions have been made, rather than analysis of the infection of most of the damage occurs after the damaged except for the case of the expert by way of conventional post-countermeasure. In this paper, we implement a mobile-based malware analysis systems apply a virtualization technology. It is designed to analyze the behavior through it. Virtualization is a technique that provides a logical resources to the guest by abstracting the physical characteristics of computing resources. The virtualization technology can improve the efficiency of resources by integrating with cloud computing services to servers, networks, storage, and computing resources to provide a flexible. In addition, we propose a system that can be prepared in advance to buy a security from a user perspective.

• Journal of Convergence for Information Technology
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• v.12 no.3
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• pp.17-24
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• 2022

Mobile malicious apps are increasing rapidly, and Android, which accounts for most of the global mobile OS market, is becoming a major target of mobile cyber security threats. Therefore, in order to cope with rapidly evolving malicious apps, there is a need for detection techniques of malicious apps using machine learning, one of artificial intelligence implementation technologies. In this paper, we propose a selected feature method using feature selection and feature extraction that can improve the detection performance of malicious apps. In the feature selection process, the detection performance improved according to the number of features, and the API showed relatively better detection performance than the permission. Also combining the two characteristics showed high precision of over 93% on average, confirming that the appropriate combination of characteristics could improve the detection performance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.455-457
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• 2021

App is used on mobile devices such as smartphones and also has malicious code, which can be divided into normal and malicious depending on the presence or absence of hacking codes. Because there are many kind of malware, it is difficult to detect directly, we propose a method to detect malicious app using AI. Most of the existing methods are to detect malicious app by extracting features from malicious app. However, the number of types have increased exponentially, making it impossible to detect malicious code. Therefore, we would like to propose two more methods besides detecting malicious app by extracting features from most existing malicious app. The first method is to learn normal app to extract normal's features, as opposed to the existing method of learning malicious app and find abnormalities (malicious app). The second one is an 'ensemble technique' that combines the existing method with the first proposal. These two methods need to be studied so that they can be used in future mobile environment.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.9
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• pp.1103-1107
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• 2016

Network assets have been protected from malware infection by checking the integrity of mobile devices through network access control systems, vaccines, or mobile device management. However, most of existing systems apply a uniform security policy to all users, and allow even infected mobile devices to log into the network inside for completion of the integrity checking, which makes it possible that the infected devices behave maliciously inside the network. Therefore, this paper proposes a network defense mechanism based on isolated networks. In the proposed mechanism, every mobile device go through the integrity check system implemented in an isolated network, and can get the network access only if it has been validated successfully.

• Journal of Internet of Things and Convergence
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• v.7 no.3
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• pp.1-8
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• 2021

With the rapid increase in the use of IoT and mobile devices, cyber criminals targeting IoT devices are also on the rise. Among IoT devices, when using a wireless access point (AP), problems such as packets being exposed to the outside due to their own security vulnerabilities or easily infected with malicious codes such as bots, causing DDoS attack traffic, are being discovered. Therefore, in this study, in order to actively respond to cyber attacks targeting IoT devices that are rapidly increasing in recent years, we proposed a method to collect traces of intrusion incidents artifacts from IoT devices, and to improve the validity of intrusion analysis data. Specifically, we presented a method to acquire and analyze digital forensics artifacts in the compromised system after identifying the causes of vulnerabilities by reproducing the behavior of the sample IoT malware. Accordingly, it is expected that it will be possible to establish a system that can efficiently detect intrusion incidents on targeting large-scale IoT devices.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.1
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• pp.169-176
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• 2016

Android platform is designed to be user-friendly, yet sometimes its convenience introduces vulnerabilities that normal users cannot justify. In this paper, after making an overview of popular open source analysis tools for android applications, we point out the dangerous use of Permission Group in current Google Policy, and suggest a technique to mitigate the risks of privilege escalation that attackers are taking advantage of. By conducting the investigation of 21,064 malware samples, we conclude that the proposed technique is considered effective in detecting insecure application update, as well as giving users the heads-up in security awareness.