• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.3
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• pp.89-100
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• 2011

As the Internet usage with web browser is more increasing, the web-based malware which is distributed in websites is going to more serious problem than ever. The central type malicious website detection method based on crawling has the problem that the cost of detection is increasing geometrically if the crawling level is lowered more. In this paper, we proposed a security tool based on web browser which can detect the malicious web pages dynamically and support user's safe web browsing by stopping navigation to a certain malicious URL injected to those web pages. By applying these tools with many distributed web browser users, all those users get to participate in malicious website detection and feedback. As a result, we can detect the lower link level of websites distributed and dynamically.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.6
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• pp.907-913
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• 2022

Approximately 70% of smartphone users around the world use Android operating system-based smartphones, and malicious apps targeting these Android platforms are constantly increasing. Google has provided "Google Play Protect" to respond to the increasing number of Android targeted malware, preventing malicious apps from being installed on smartphones, but many malicious apps are still normal. It threatens the smartphones of ordinary users registered in the Google Play store by disguising themselves as apps. However, most people rely on antivirus programs to detect malicious apps because the average user needs a great deal of expertise to check for malicious apps. Therefore, in this paper, we propose a method to classify unnecessary malicious permissions of apps by using only the categories and permissions that can be easily confirmed by the app, and to easily detect malicious apps through the classified permissions. The proposed method is compared and analyzed from the viewpoint of undiscovered rate and false positives with the "commercial malicious application detection program", and the performance level is presented.

• International Journal of Computer Science & Network Security
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• v.21 no.12
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• pp.213-218
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• 2021

This paper proposes a technique for detecting a significant threat that attempts to get sensitive and confidential information such as usernames, passwords, credit card information, and more to target an individual or organization. By definition, a phishing attack happens when malicious people pose as trusted entities to fraudulently obtain user data. Phishing is classified as a type of social engineering attack. For a phishing attack to happen, a victim must be convinced to open an email or a direct message [1]. The email or direct message will contain a link that the victim will be required to click on. The aim of the attack is usually to install malicious software or to freeze a system. In other instances, the attackers will threaten to reveal sensitive information obtained from the victim. Phishing attacks can have devastating effects on the victim. Sensitive and confidential information can find its way into the hands of malicious people. Another devastating effect of phishing attacks is identity theft [1]. Attackers may impersonate the victim to make unauthorized purchases. Victims also complain of loss of funds when attackers access their credit card information. The proposed method has two major subsystems: (1) Data collection: different websites have been collected as a big data corresponding to normal and phishing dataset, and (2) distributed detection system: different artificial algorithms are used: a neural network algorithm and machine learning. The Amazon cloud was used for running the cluster with different cores of machines. The experiment results of the proposed system achieved very good accuracy and detection rate as well.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.6
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• pp.1133-1146
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• 2011

Cognitive radio (CR) is considered one of the most promising next-generation communication systems; it has the ability to sense and make use of vacant channels that are unused by licensed users. Reliable detection of the licensed users' signals is an essential element for a CR network. Cooperative spectrum sensing (CSS) is able to offer better sensing performance as compared to individual sensing. The presence of malicious users who falsify sensing data can severely degrade the sensing performance of the CSS scheme. In this paper, we investigate a secure CSS scheme, based on the Kullback-Leibler Divergence (KL-divergence) theory, in order to identify malicious users and mitigate their harmful effect on the sensing performance of CSS in a CR network. The simulation results prove the effectiveness of the proposed scheme.

• Convergence Security Journal
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• v.15 no.1
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• pp.3-9
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• 2015

In this paper, a detection technique of smishing using a TaintDroid is suggested. Suggesting system detects malicious acts by transmitting a URL to the TaintDroid server and installing a relevant application to a virtual device of the TaintDroid server, when a smartphone user receives a text message including the URL suspected as a smishing. Through this we want to distinguish an application that can not install because of suspicion of a smishing in an actual smartphone whether said application is malicious application or not by testing with the virtual device of said system. The detection technique of a smishing using the TaintDroid suggested in this paper is possible to detect in a new form a smishing with a text message and to identifying which application it is through analysis of results from a user.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.4
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• pp.405-414
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• 2010

According to the statistics of SecurityFocus in 2008, client-side attacks through the Microsoft Internet Explorer have increased by more than 50%. In this paper, we have implemented a behavior-based malicious web page detection system and a blacklist-based malicious web page filtering system. To do this, we first efficiently collected the target URLs by constructing a crawling system. The malicious URL detection system, run on a specific server, visits and renders actively the collected web pages under virtual machine environment. To detect whether each web page is malicious or not, the system state changes of the virtual machine are checked after rendering the page. If abnormal state changes are detected, we conclude the rendered web page is malicious, and insert it into the blacklist of malicious web pages. The malicious URL filtering system, run on the web client machine, filters malicious web pages based on the blacklist when a user visits web sites. We have enhanced system performance by automatically handling message boxes at the time of ULR analysis on the detection system. Experimental results show that the game sites contain up to three times more malicious pages than the other sites, and many attacks incur a file creation and a registry key modification.

• Journal of the Korea Society of Computer and Information
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• v.18 no.11
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• pp.125-136
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• 2013

In this paper, we proposed SSL VPN-based network access control technology which can verify user authentication and integrity of user terminal. Using this technology, user can carry out a safety test to check security services such as security patch and virus vaccine for user authentication and user terminal, during the VPN-based access to an internal network. Moreover, this system protects a system from external security threats, by detecting malicious codes, based on behavioral patterns from user terminal's window API information, and comparing the similarity of sequential patterns to improve the reliability of detection.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1657-1673
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• 2023

With the advancement of information technology, criminals employ multiple cyberspaces to promote cybercrime. To combat cybercrime and cyber dangers, banks and financial institutions use artificial intelligence (AI). AI technologies assist the banking sector to develop and grow in many ways. Transparency and explanation of AI's ability are required to preserve trust. Deep learning protects client behavior and interest data. Deep learning techniques may anticipate cyber-attack behavior, allowing for secure banking transactions. This proposed approach is based on a user-centric design that safeguards people's private data over banking. Here, initially, the attack data can be generated over banking transactions. Routing is done for the configuration of the nodes. Then, the obtained data can be preprocessed for removing the errors. Followed by hierarchical network feature extraction can be used to identify the abnormal features related to the attack. Finally, the user data can be protected and the malicious attack in the transmission route can be identified by using the Wrapper stepwise ResNet classifier. The proposed work outperforms other techniques in terms of attack detection and accuracy, and the findings are depicted in the graphical format by employing the Python tool.

• Journal of the Korea Convergence Society
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• v.8 no.5
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• pp.37-43
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• 2017

A registry is a hierarchy database which is designed to store information necessary for operating system and application programs in Windows operating system, and it is involved in all activities such as booting, logging, service execution, application execution, and user behavior. Digital forensic is widely used. In recent years, malicious codes have penetrated into systems in a way that is not recognized by the user, and valuable information is leaked or stolen, causing financial damages. Therefore, this study proposes a method to detect malicious code by using a shareware application without using expensive digital forensic program, so as to analysis hacking methods and prevent hacking damage in advance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4684-4705
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• 2019

Collaborative filtering recommender systems are vulnerable to shilling attacks in which malicious users may inject biased profiles to promote or demote a particular item being recommended. To tackle this problem, many robust collaborative recommendation methods have been presented. Unfortunately, the robustness of most methods is improved at the expense of prediction accuracy. In this paper, we construct a robust Bayesian probabilistic matrix factorization model for collaborative filtering recommender systems by incorporating the detection of user anomaly rating behaviors. We first detect the anomaly rating behaviors of users by the modified K-means algorithm and target item identification method to generate an indicator matrix of attack users. Then we incorporate the indicator matrix of attack users to construct a robust Bayesian probabilistic matrix factorization model and based on which a robust collaborative recommendation algorithm is devised. The experimental results on the MovieLens and Netflix datasets show that our model can significantly improve the robustness and recommendation accuracy compared with three baseline methods.