• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.3
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• pp.667-677
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• 2012

Vulnerable web applications have been the primary method used by the attackers to spread their malware to a large number of victims. Such attacks commonly make use of malicious links to remotely execute a rather advanced malicious code. The attackers often deploy malwares that utilizes unknown vulnerabilities so-called "zero-day vulnerabilities." The existing computer vaccines are mostly signature-based and thus are effective only against known attack patterns, but not capable of detecting zero-days attacks. To mitigate such limitations of the current solutions, there have been a numerous works that takes a behavior-based approach to improve detection against unknown malwares. However, behavior-based solutions arbitrarily introduced a several limitations that made them unsuitable for real-life situations. This paper proposes an advanced web browser based malicious behavior detection system that solves the problems and limitations of the previous approaches.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.182-186
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• 2021

The market for smart phones has been booming in the past few years. There are now over 400,000 applications on the Android market. Over 10 billion Android applications have been downloaded from the Android market. Due to the Android popularity, there are now a large number of malicious vendors targeting the platform. Many honest end users are being successfully hacked on a regular basis. In this work, a cloud based reputation security model has been proposed as a solution which greatly mitigates the malicious attacks targeting the Android market. Our security solution takes advantage of the fact that each application in the android platform is assigned a unique user id (UID). Our solution stores the reputation of Android applications in an anti-malware providers' cloud (AM Cloud). The experimental results witness that the proposed model could well identify the reputation index of a given application and hence its potential of being risky or not.

• Journal of Internet Computing and Services
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• v.21 no.1
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• pp.45-55
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• 2020

Due to the development of information and communication technology, the number of new / variant malicious codes is increasing rapidly every year, and various types of malicious codes are spreading due to the development of Internet of things and cloud computing technology. In this paper, we propose a malware analysis method based on string information that can be used regardless of operating system environment and represents library call information related to malicious behavior. Attackers can easily create malware using existing code or by using automated authoring tools, and the generated malware operates in a similar way to existing malware. Since most of the strings that can be extracted from malicious code are composed of information closely related to malicious behavior, it is processed by weighting data features using text mining based method to extract them as effective features for malware analysis. Based on the processed data, a model is constructed using various machine learning algorithms to perform experiments on detection of malicious status and classification of malicious groups. Data has been compared and verified against all files used on Windows and Linux operating systems. The accuracy of malicious detection is about 93.5%, the accuracy of group classification is about 90%. The proposed technique has a wide range of applications because it is relatively simple, fast, and operating system independent as a single model because it is not necessary to build a model for each group when classifying malicious groups. In addition, since the string information is extracted through static analysis, it can be processed faster than the analysis method that directly executes the code.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.180-187
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• 2020

Security threats are increasing with interest due to the mass spread of smart devices, and vulnerabilities in developed applications are being exposed while mobile malicious codes are spreading. The government and companies provide various applications for the public, and for reliability and security of applications, security checks are required during application development. In this paper, among the security threats that can occur in the mobile service environment, we set up the vulnerability analysis items to respond to security threats when developing Android-based applications. Based on the set analysis items, vulnerability analysis was performed by examining three applications of public institutions and private companies currently operating as mobile applications. As a result of application security checks used by three public institutions and companies, authority management and open module stability management were well managed. However, it was confirmed that many security vulnerabilities were found in input value verification, outside transmit data management, and data management. It is believed that it will contribute to improving the safety of mobile applications through the case of vulnerability analysis for Android application security.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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• pp.1533-1556
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• 2017

The concept of trust in vehicular ad hoc networks (VANETs) is usually utilized to assess the trustworthiness of the received data as well as that of the sending entities. The quality of safety applications in VANETs largely depends on the trustworthiness of exchanged data. In this paper, we propose a self-organized distributed trust computing framework (DTCF) for VANETs to compute the trustworthiness of each vehicle, in order to filter out malicious nodes and recognize fully trusted nodes. The proposed framework is solely based on the investigation of the direct experience among vehicles without using any recommendation system. A tier-based dissemination technique for data messages is used to filter out non authentic messages and corresponding events before even going farther away from the source of the event. Extensive simulations are conducted using Omnet++/Sumo in order to investigate the efficiency of our framework and the consistency of the computed trust metrics in both urban and highway environments. Despite the high dynamics in such networks, our proposed DTCF is capable of detecting more than 85% of fully trusted vehicles, and filtering out virtually all malicious entities. The resulting average delay to detect malicious vehicles and fraudulent data is showed to be less than 1 second, and the computed trust metrics are shown to be highly consistent throughout the network.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.6
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• pp.1215-1224
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• 2014

Various research was done to protect user's private data from malicious application which expose user's private data and abuse exposed data. However, a new type of malicious application were appeared. And these malicious applications use a smart phone as a new tools to perform secondary attack. Therefore, in this paper, we propose a method to detect the DDoS attack application installed inside the mobile device using the Android logging system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.4191-4203
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• 2015

WebView is a vital component in smartphone platforms like Android, Windows and iOS that enables smartphone applications (apps) to embed a simple yet powerful web browser inside them. WebView not only provides the same functionalities as web browser, it, more importantly, enables a rich interaction between apps and webpages loaded inside the WebView. However, the design and the features of WebView lays path to tamper the sandbox protection mechanism implemented by browsers. As a consequence, malicious attacks can be launched either against the apps or by the apps through the exploitation of WebView APIs. This paper presents a critical attack called Supplementary Event-Listener Injection (SEI) attack which adds auxiliary event listeners, for executing malicious activities, on the HTML elements in the webpage loaded by the WebView via JavaScript Injection. This paper also proposes an automated static analysis system for analyzing WebView embedded apps to classify the kind of vulnerability possessed by them and a solution for the mitigation of the attack.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3750-3770
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• 2021

This study investigates open-source dynamic XSS filters used as security devices in web applications to account for the effectiveness of filters in protecting against XSS attacks. The experiment involves twelve representative filters, which are examined individually by placing them into the final output function of a custom-built single-input-form web application. To assess the effectiveness of the filters in their tasks of sanitizing XSS payloads and in preserving benign payloads, a black-box testing method is applied using an automated XSS testing framework. The result in working with malicious and benign payloads shows an important trade-off in the filters' tasks. Because the filters that only check for dangerous or safe elements, they seem to neglect to validate their values. As some safe values are mistreated as dangerous elements, their benign payload function is lost in the way. For the filters to be more effective, it is suggested that they should be able to validate the respective values of malicious and benign payloads; thus, minimizing the trade-off. This particular assessment of XSS filters provides important insight regarding the filters that can be used to mitigate threats, including the possible configurations to improve them in handling both malicious and benign payloads.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3420-3436
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• 2020

Due to the increasing number of malicious software (also known as malware), methods for sharing threat information are being studied by various organizations. The Malware Attribute Enumeration and Characterization (MAEC) format of malware is created by analysts, converted to Structured Threat Information Expression (STIX), and distributed by using Trusted Automated eXchange of Indicator Information (TAXII) protocol. Currently, when sharing malware analysis results, analysts have to manually input them into MAEC. Not many analysis results are shared publicly. In this paper, we propose an automated MAEC conversion technique for sharing analysis results of malicious Android applications. Upon continuous research and study of various static and dynamic analysis techniques of Android Applications, we developed a conversion tool by classifying parts that can be converted automatically through MAEC standard analysis, and parts that can be entered manually by analysts. Also using MAEC-to-STIX conversion, we have discovered that the MAEC file can be converted into STIX. Although other researches have been conducted on automatic conversion techniques of MAEC, they were limited to Windows and Linux only. In further verification of the conversion rate, we confirmed that analysts could improve the efficiency of analysis and establish a faster sharing system to cope with various Android malware using our proposed technique.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.4
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• pp.617-624
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• 2014

Smart phone usage has increased exponentially and open source based Android OS occupy significant market share. However, various malicious applications that use the characteristic of Android threaten users. In this paper, we construct an efficient malicious application detector by using the principle component analysis and the incremental k nearest neighbor algorithm, which consider an required permission, of Android applications. The cross validation is exploited in order to find a critical parameter of the algorithm. For the performance evaluation of our approach, we simulate a real data set of Contagio Mobile.