• Journal of KIISE:Computer Systems and Theory
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• v.34 no.10
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• pp.502-510
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• 2007

Private Matching is a problem of computing the intersection of private datasets of two parties. One could envision the usage of private matching for Insurance fraud detection system, Do-not-fly list, medical databases, and many other applications. In 2004, Freedman et at. [1] introduced a probabilistic solution for this problem, and they extended it to malicious adversary model and multi-party computation. In this paper, we propose a new deterministic protocol for private matching with perfect correctness. We apply this technique to adversary models, achieving more reliable and higher speed computation.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.2
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• pp.279-292
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• 2015

In this study, we propose an effective network managed security services model that can detect a presence of potential malicious codes inside the energy-based SCADA Systems. Especially, by analyzing the data obtained in the same environment of SCADA Systems, we develop detection factors to applicable to the managed security services and propose the method for the network managed security services. Finally, the proposed network managed security services model through simulation proved possibility to detect malicious traffic in SCADA systems effectively.

• Convergence Security Journal
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• v.6 no.2
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• pp.31-42
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• 2006

In this paper, we proposed an integrated infrastructure for optimal information security to resolve these kinds of problems and to implement more powerful protection. The proposed infrastructure presents a security framework, provides a functional mechanism, and implements a scheme for information security based on the design concept of integrated structures. In order to ensure effective malicious traffic blocking, this paper emphasizes that a comprehensive approach through infrastructure improvement and combination of scanning tool is the only measure for preparing against today's environment of virus infiltration. The proposed model is a measure developed at a time when a permanent technological solution to virus is yet to be developed. A performance analysis model is developed and the performance is evaluated through the case studies for the proposed methodology. The effectiveness of the infrastructure for optimal information security needs the continuous diagnostic evaluation and tuning through the users or the organizations.

• 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.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.186-192
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• 2023

Malwares are becoming a major problem nowadays all around the world in android operating systems. The malware is a piece of software developed for harming or exploiting certain other hardware as well as software. The term Malware is also known as malicious software which is utilized to define Trojans, viruses, as well as other kinds of spyware. There have been developed many kinds of techniques for protecting the android operating systems from malware during the last decade. However, the existing techniques have numerous drawbacks such as accuracy to detect the type of malware in real-time in a quick manner for protecting the android operating systems. In this article, the authors developed a hybrid model for android malware detection using a decision tree and KNN (k-nearest neighbours) technique. First, Dalvik opcode, as well as real opcode, was pulled out by using the reverse procedure of the android software. Secondly, eigenvectors of sampling were produced by utilizing the n-gram model. Our suggested hybrid model efficiently combines KNN along with the decision tree for effective detection of the android malware in real-time. The outcome of the proposed scheme illustrates that the proposed hybrid model is better in terms of the accurate detection of any kind of malware from the Android operating system in a fast and accurate manner. In this experiment, 815 sample size was selected for the normal samples and the 3268-sample size was selected for the malicious samples. Our proposed hybrid model provides pragmatic values of the parameters namely precision, ACC along with the Recall, and F1 such as 0.93, 0.98, 0.96, and 0.99 along with 0.94, 0.99, 0.93, and 0.99 respectively. In the future, there are vital possibilities to carry out more research in this field to develop new methods for Android malware detection.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.9
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• pp.2285-2301
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• 2012

Peer-to-Peer (P2P) networks are becoming more and more popular in video content delivery services, such as Video on Demand (VoD). Scalability feature of P2P allows a higher number of simultaneous users at a given server load and bandwidth to use stream service. However, the quality of service (QoS) in these networks is difficult to be guaranteed because of the free-riding problem that nodes download the recourses while never uploading recourses, which degrades the performance of P2P VoD networks. In this paper, a distributed trust model is designed to reduce node's free-riding phenomenon in P2P VoD networks. In this model, the P2P network is abstracted to be a super node hierarchical structure to monitor the reputation of nodes. In order to calculate the reputation of nodes, the Hidden Markov Model (HMM) is introduced in this paper. Besides, a distinction algorithm is proposed to distinguish the free-riders and malicious nodes. The free-riders are the nodes which have a low frequency to free-ride. And the malicious nodes have a high frequency to free-ride. The distinction algorithm takes different measures to response to the request of these two kinds of free-riders. The simulation results demonstrate that this proposed trust model can improve QoS effectively in P2P VoD networks.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.6
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• pp.1105-1114
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• 2021

Due to the recent surge in popularity of cryptocurrency, the threat of cryptojacking, a malicious code for mining cryptocurrencies, is increasing. In particular, web-based cryptojacking is easy to attack because the victim can mine cryptocurrencies using the victim's PC resources just by accessing the website and simply adding mining scripts. The cryptojacking attack causes poor performance and malfunction. It can also cause hardware failure due to overheating and aging caused by mining. Cryptojacking is difficult for victims to recognize the damage, so research is needed to efficiently detect and block cryptojacking. In this work, we take representative distinct symptoms of cryptojacking as an indicator and propose a new architecture. We utilized the K-Nearst Neighbors(KNN) model, which trained computer performance indicators as behavior-based dynamic analysis techniques. In addition, a K-means model, which trained the frequency of malicious script words for script similarity-based static analysis techniques, was utilized. The KNN model had 99.6% accuracy, and the K-means model had a silhouette coefficient of 0.61 for normal clusters.

• Convergence Security Journal
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• v.22 no.1
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• pp.19-27
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• 2022

With the innovative development of ICT technology, hacking techniques of hackers are also evolving into sophisticated and intelligent hacking techniques. Threat detection research to counter these cyber threats was mainly conducted in a passive way through hacking damage investigation and analysis, but recently, the importance of cyber threat information collection and analysis is increasing. A bot-type automation program is a rather active method of extracting malicious code by visiting a website to collect threat information or detect threats. However, this method also has a limitation in that it cannot prevent hacking damage because it is a method to identify hacking damage because malicious code has already been distributed or after being hacked. Therefore, to overcome these limitations, we propose a model that detects actual threats by acquiring and analyzing threat information while identifying and managing cyber bases. This model is an active and proactive method of collecting threat information or detecting threats outside the boundary such as a firewall. We designed a model for detecting threats using cyber strongholds and validated them in the defense environment.

• Convergence Security Journal
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• v.14 no.2
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• pp.17-24
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• 2014

Form of backdoor penetration attacks "trapdoor" penetration points to bypass the security features and allow direct access to the data. Backdoor without modifying the source code is available, and even code generation can also be modified after compilation. This approach by rewriting the compiler when you compile the source code to insert a specific area in the back door can be due to the use of the method. Defense operations and the basic structure of the backdoor or off depending on the nature of the damage area can be a little different way. This study is based on the diagnosis of a back door operating mechanism acting backdoor analysis methods derived. Research purposes in advance of the attack patterns of malicious code can respond in a way that is intended to be developed. If we identify the structures of backdoor and the infections patterns through the analysis, in the future we can secure the useful information about malicious behaviors corresponding to hacking attacks.

• Convergence Security Journal
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• v.20 no.3
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• pp.53-60
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• 2020

At the present stage of the fourth industrial revolution, machine learning and artificial intelligence technologies are rapidly developing, and there is a movement to apply machine learning technology in the security field. Malicious code, including new and transformed, generates an average of 390,000 a day worldwide. Statistics show that security companies ignore or miss 31 percent of alarms. As many malicious codes are generated, it is becoming difficult for humans to detect all malicious codes. As a result, research on the detection of malware and network intrusion events through machine learning is being actively conducted in academia and industry. In international conferences and journals, research on security data analysis using deep learning, a field of machine learning, is presented. have. However, these papers focus on detection accuracy and modify several parameters to improve detection accuracy but do not consider the ratio of dataset. Therefore, this paper aims to reduce the cost and resources of many machine learning research by finding the ratio of dataset that can derive the highest detection accuracy in CNN Mobile net-based malware detection model.