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Kim M.,Cyber Security Convergence Research Laboratory | Han J.W.,Cyber Security Convergence Research Laboratory
International Conference on ICT Convergence | Year: 2012

This paper presents an optimized architecture for a first-one detector (FOD) using a uniform partition decoding scheme based on the statistical distribution of the input code words. The proposed architecture uses a conventional method to optimize the Boolean expression of the input code words. Experimental results show that the proposed approach covers only 58 gates in a 0.25 μm CMOS technology. Based on the result of our design, we can achieve a remarkable reduction of a hardware cost, which consumes less than 10% of the size of the implementation of previous works. © 2012 IEEE.


Kang Y.S.,Cyber Security Convergence Research Laboratory | Choi D.,Cyber Security Convergence Research Laboratory | Park D.-J.,KAIST
Proceedings - 2012 7th International Conference on Computing and Convergence Technology (ICCIT, ICEI and ICACT), ICCCT 2012 | Year: 2012

The conventional works on secret key distribution in the wireless sensor network have mainly focused on the application layer approaches. Recently, some researchers are studying the more efficient key distribution using the characteristics of lower layers such as network layer and physical layer. Oliveira and Barros proposed an approach to secret key distribution based on network coding of the network layer in 2008. The Oliveira-Barros scheme is a robust and low-complexity solution for sharing secret keys among sensor nodes, but every node in this scheme exposes its own secret key to other sensor nodes. In this paper, we identify two weak points resulting from the disclosure of the secret key and a flaw in their cluster key distribution scheme. We then propose security-enhanced schemes for session key and cluster key establishment using network coding operations. © 2012 AICIT.


Ham Y.J.,Hanshin University | Lee H.-W.,Hanshin University | Lim J.D.,Cyber Security Convergence Research Laboratory | Kim J.N.,Cyber Security Convergence Research Laboratory
International Conference on Next Generation Mobile Applications, Services, and Technologies | Year: 2013

The use of Android platform based mobile terminals has been growing high. On the down side, the number of attacks by malicious application is also increasing because Android platform is vulnerable to private information leakage. These malicious applications are easily distributed to users through open market or internet. An attacker inserts malicious code into mobile app which could be harmful tool to steal private data. To cope with security threats more actively, it is necessary to develop countermeasure system that enables to detect security vulnerability existing in mobile terminal and take an appropriate action to protect the system against malicious attacks. Therefore, this paper investigates security-related vulnerability for mobile terminal and suggests 'DroidVulMon' system to detect and respond attacks in order to prevent information leakage caused by malicious app. © 2013 IEEE.


Kim S.,Hanshin University | Moon D.,Hanshin University | Lee H.-W.,Hanshin University | Lim J.D.,Cyber Security Convergence Research Laboratory | Kim J.N.,Cyber Security Convergence Research Laboratory
Advanced Science Letters | Year: 2015

Malicious applications (apps) are easily distributed to general users through open markets or the Internet. An attacker inserts Server-Side Polymorphic (SSP) code into malicious apps that could be used to steal private and certificate data stored on user smart devices. To cope with SSP malicious threats more accurately, it is necessary to develop a countermeasure mechanism that allows the detection of hidden evasive SSP malicious code in order to protect mobile devices against remotely operated attacks. This paper proposes a mechanism to detect SSP malicious apps by measuring the dynamic system call property of Android smartphone devices. © 2015, American Scientific Publishers. All rights reserved.


Ham Y.-J.,Hanshin University | Choi W.-B.,Hanshin University | Lee H.-W.,Hanshin University | Lim J.,Cyber Security Convergence Research Laboratory | Kim J.N.,Cyber Security Convergence Research Laboratory
Proceedings of 2nd International Conference on Computer Science and Network Technology, ICCSNT 2012 | Year: 2012

The amount of malicious mobile application targeting Android based smartphones has increase rapidly. In addition, these malicious apps are capable of downloading modules from servers which are run by malicious users, meaning that unexpected events can be activated inside of smartphones. Therefore, the attacker can control and get personal information and data stored inside of smartphone illegally. Therefore, it is necessary to monitor several event-driven activities and to detect malicious service for degrading the vulnerability on Android based smartphone. The correlation analysis mechanism is the use of statistical and systemic data to evaluate the relations between several variables. Therefore, we propose vulnerability monitoring mechanism with correlation analysis on event-driven activities in Android platform. In first, the basic activity data set (application integrity information, real-time process list, network connection and activated service list) are constructed as a DB server. And then the event data generated inbound of smartphone are aggregated and updated periodically. Based on these data, correlation analysis process is performed to detect malicious activity such as rooting attack triggered by rootkit for acquisition of administration permission. Therefore, it is useful to decrease a threat by detecting malicious event on Android based smartphones. © 2012 IEEE.


Ham Y.J.,Hanshin University | Moon D.,Hanshin University | Lee H.-W.,Hanshin University | Lim J.D.,Cyber Security Convergence Research Laboratory | Kim J.N.,Cyber Security Convergence Research Laboratory
International Journal of Security and its Applications | Year: 2014

Due to the openness of the Android-based open market, the distribution of malicious applications developed by attackers is increasing rapidly. In order to reduce the damage caused by the malicious applications, the mechanism that allows more accurate way to determine normal apps and malicious apps for common mobile devices should be developed. In this paper, the normal system call event patterns were analyzed from the most highly used game app in the Android open market, and the malicious system call event patterns were also analyzed from the malicious game apps extracted from 1260 malware samples distributed by Android MalGenome Project. Using the Strace tool, system call events are aggregated from normal and malicious apps. And analysis of relevance to each event set was performed. Through this process of analyzing the system call events, we can extract a similarity to determine whether any given app is malicious or not. © 2014 SERSC.


Lim J.,Cyber Security Convergence Research Laboratory | Lee C.,Korea University | Choi B.,Cyber Security Convergence Research Laboratory | Han S.,Cyber Security Convergence Research Laboratory | Kim J.,Cyber Security Convergence Research Laboratory
International Journal of Advancements in Computing Technology | Year: 2012

This paper proposes the automatic X-rated video classification and management system and its implementation. Automatic video classification is based on multimodal features; visual and auditory features. Visual features consist of image-based feature and video-based feature. Some MPEG-7 image descriptors and the rule of thirds are adopted as imaged-based feature for deciding harmfulness of a single video frame. The temporal color histogram feature and the repeated curve-like spectrum feature are used as video-based and audio-based feature respectively. They can detect efficiently the motion and sound properties that are appeared in most indecent scenes. In classifying a video file, we use multi-level decision and classification; feature-level decision, clip-level decision and finally file-level classification. Support vector machine classifier is used at the feature-level decision. Each single feature-based video classification performance has about or a slightly higher than 90% of accuracy and multimodal feature-based video classification performance is improved up to 96.5% of accuracy under our dataset configured with 500 general videos and 500 X-rated videos. The measured performance shows that multimodal approach can be deployed to improve the classification performance. The proposed system also provides the graphical interfaces for classification and management in order to verify and handle the detailed classification results of each video.

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