• Journal of the Korea Convergence Society
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• v.5 no.2
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• pp.1-6
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• 2014

Latest Smart TV is rapidly increasing in prevalence. Smart-TV can provide reserve-service, training-service, and game service to user. Smart-TV need standard API for user requested app to run it. In this case, the cracker can run Malicious apps or user-request contents to modify it by analysis of the vulnerability of the smart-TV platform. Therefore, we modify the extension API on Smart-TV to prevent hacking. Our proposed research method can support that device is illegal in Smart-TV device can prevent unauthorized access.

• The KIPS Transactions:PartC
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• v.11C no.3
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• pp.319-326
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• 2004

To help network intrusion detection systems(NIDSs) keep up with the demands of today's networks, that we the increasing network throughput and amount of attacks, a radical new approach in hardware and software system architecture is required. In this paper, we propose a Network Processor(NP) based In-Line mode NIDS that supports the packet payload inspection detecting the malicious behaviors, as well as the packet filtering and the traffic metering. In particular, we separate the filtering and metering functions from the deep packet inspection function using two-level searching scheme, thus the complicated and time-consuming operation of the deep packet inspection function does not hinder or flop the basic operations of the In-line mode system. From a proto-type NP-based NIDS implemented at a PC platform with an x86 processor running Linux, two Gigabit Ethernet ports, and 2.5Gbps Agere PayloadPlus(APP) NP solution, the experiment results show that our proposed scheme can reliably filter and meter the full traffic of two gigabit ports at the first level even though it can inspect the packet payload up to 320 Mbps in real-time at the second level, which can be compared to the performance of general-purpose processor based Inspection. However, the simulation results show that the deep packet searching is also possible up to 2Gbps in wire speed when we adopt 10Gbps APP solution.

• Journal of Service Research and Studies
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• v.5 no.2
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• pp.111-118
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• 2015

Although the development of mobile devices are made us a free life, they were displayed the subject of this financial crime and attacking forces in the other side. Among finance-related crime is become a serious crime that are targeting smartphones by SMS phishing, phishing, pharming, voice phishing etc. In particular, SMS phishing is increased according to phenomenon using the nature of a text message in the mobile. SMS phishing is become new crime due to the burden to the smartphone user. Their crime is also the advanced way from the existing fraud, such as making the malicious apps. Especially it generates loopholes in the law by a method such as using a foreign server. For safe from SMS phishing attacks, proactive pre-diagnosis is even more important rather than post responses. It is necessary to deploy blocking programs for detecting SMS phishing attacks in advance to do this. In this paper we are investigating the process of block types and block apps that are currently deployed and presenting the evaluation of the application of the detection block setting app.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.5
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• pp.1235-1241
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• 2016

Personalized App recommendation system is recently famous since the number of various apps that can be used in smart phones that increases exponentially. However, the site users using google play site with malwares have experienced severe damages of privacy exposure and extortion as well as a simple damage of satisfaction descent at the same time. In addition, Sybil attack (Sybil) manipulating the score (rating) of each app with falmay also present because of the social networks development. Up until now, the sybil detection studies and malicious apps studies have been conducted independently. But it is important to determine finally the existence of intelligent attack with Sybil and malware simultaneously when we consider the intelligent attack types in real-time. Therefore, in this paper we experimentally evaluate the relationship between malware and sybils based on real cralwed dataset of goodlplay. Through the extensive evaluations, the correlation between malware and sybils is low for malware providers to hide themselves from Anti-Virus (AV).

• Journal of Internet Computing and Services
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• v.13 no.2
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• pp.51-57
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• 2012

The warming up of the recent competition in the smart phone industry followed by rapid increase of open platforms and app stores have led the mobile terminals to adopt the general purpose operating system. The mobile device utilizing this OS is vulnerable to mobile malignant code which is highly transferable. As a result, the scale of mobile attack and harm increase. However, compared to the malicious code and virus which keep increasing, the elements for security to prevent are insufficient. Therefore, this paper describe about the Context Aware Service that eliminates the potentially risky elements on the smart phone service, which could threaten the usability of the service including invasion of personal information. The proposed system prevents from disclosure of personal information by giving the procedure of information, and realizes the service that is able to change the sequence of identifying the users' information to prepare for emergency.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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• pp.3230-3253
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• 2017

Android malware steals users' private information, and embedded unsafe advertisement (ad) libraries, which execute unsafe code causing damage to users. The majority of such traffic is HTTP and is mixed with other normal traffic, which makes the detection of malware and unsafe ad libraries a challenging problem. To address this problem, this work describes a novel HTTP traffic flow mining approach to detect and categorize Android malware and unsafe ad library. This work designed AndroCollector, which can automatically execute the Android application (app) and collect the network traffic traces. From these traces, this work extracts HTTP traffic features along three important dimensions: quantitative, timing, and semantic and use these features for characterizing malware and unsafe ad libraries. Based on these HTTP traffic features, this work describes a supervised classification scheme for detecting malware and unsafe ad libraries. In addition, to help network operators, this work describes a fine-grained categorization method by generating fingerprints from HTTP request methods for each malware family and unsafe ad libraries. This work evaluated the scheme using HTTP traffic traces collected from 10778 Android apps. The experimental results show that the scheme can detect malware with 97% accuracy and unsafe ad libraries with 95% accuracy when tested on the popular third-party Android markets.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.11
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• pp.6849-6855
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• 2014

Recently, with the increasing use of smart phones, security issues, such as safety and reliability of the use of the Android application has become a topic to provide services in various forms. An Android application is performed using several important files in the form of an apk file. On the other hand, they may be subject to unauthorized use, such as the loss of rights and privileges due to the insertion of malicious source code of these apk files. This paper examines the Android environment to study ways to define the threats related to the unauthorized use of the application source code, and based on the results of the analysis, to prevent unauthorized use of the application source code. In this paper, a system is provided using a third body to prevent and detect applications that have been counterfeited or forged illegally and installed on Android devices. The application provides services to existing systems that are configured with only the service server that provides users and applications general, This paper proposes the use of a trusted third party for user registration and to verify the integrity of the application, add an institution, and provide a safe application.

• Journal of Advanced Navigation Technology
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• v.18 no.6
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• pp.569-575
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• 2014

Since 2009 the number of users of smart phones and tablet PC is growing exponentially. In particular Apple's iOS and Google's Android OS are the heart of this remarkable growth, most of smart phone and tablet PC are designed to operate based on these two OS. Such increasing use of smart devices has led to changes in the social environment that allows, without the constraints of time and place. However, such development does not supply only ease to do something, even compared to past, financial fraud and information leakage are easier than before by variety of new types of attack for example phishing, pharming, smishing and qshing. So according to this paper, analyzes for smishing attack, propose a countermeasure system of the technical way and proved its higher performance compare to the existing method.

• Convergence Security Journal
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• v.16 no.3_2
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• pp.3-12
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• 2016

The purpose of this study is to analyse Modus Operandi of smishing. For the study, 87 cases of smishing crime reports and smishing experiences of victims were analysed and 10 police officers who investigates smishing crime were interviewed. The results indicated that smishing crime can be divided into the preparation stage and the implementation stage. In the preparation stage, two modus operandi patterns, collection of personal information and text message script composition, were identified. In the implementation stage, seven modus operandi patterns were identified: sending smishing text messages and installation of malicious mobile applications, leak personal information, sending personal information to smishing crime organization through online server, payment attempt using collected personal information, intercept authorization code, completion of payment using intercepted authorization code, and payment amount was delivered to victims. Further implications were discussed.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.3
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• pp.519-529
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• 2019

Nowadays, intelligent Android malware applies anti-analysis techniques to hide malicious behaviors and make it difficult for anti-virus vendors to detect its presence. Malware can use background components to hide harmful operations, use activity-alias to get around with automation script, or wipe the logcat to avoid forensics. During our study, several static analysis tools can not extract these hidden components like main activity, and dynamic analysis tools also have problem with code coverage due to partial execution of android malware. In this paper, we design and implement a system to analyze intelligent malware that uses anti-analysis techniques to improve detection rate of evasive malware. It extracts the hidden components of malware, runs background components like service, and generates all the intent events defined in the app. We also implemented a real-time logging system that uses modified logcat to block deleting logs from malware. As a result, we improve detection rate from 70.9% to 89.6% comparing other container based dynamic analysis platform with proposed system.