• Journal of KIISE:Computing Practices and Letters
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• v.12 no.6
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• pp.339-348
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• 2006

Recently, many researches on detecting and responding worms due to the fatal infrastructural damages explosively damaged by automated attack tools, particularly worms. Network service vulnerability exploiting worms have high propagation velocity, exhaust network bandwidth and even disrupt the Internet. Previous worm researches focused on signature-based approaches however these days, approaches based on behavioral features of worms are more highlighted because of their low false positive rate and the attainability of early detection. In this paper, we propose a Distributed Worm Detection Model based on packet marking. The proposed model detects Worm Cycle and Infection Chain among which the behavior features of worms. Moreover, it supports high scalability and feasibility because of its distributed reacting mechanism and low processing overhead. We virtually implement worm propagation environment and evaluate the effectiveness of detecting and responding worm propagation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.2B
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• pp.131-140
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• 2011

As the number of Internet users and applications is increasing, the importance of application traffic classification is growing more and more for efficient network management. While a number of methods for traffic classification have been introduced, such as signature-based and machine learning-based methods, Skype application, which uses encrypted communication on its own P2P network, is known as one of the most difficult traffic to identify. In this paper we propose a novel method to identify Skype application traffic on the fly. The main idea is to setup a list of Skype host information {IP, port} by examining the packets generated in the Skype login process and utilizes the list to identify other Skype traffic. By implementing the identification system and deploying it on our campus network, we proved the performance and feasibility of the proposed method.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.5
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• pp.517-521
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• 2008

A worm is a malware that propagates quickly from host to host without any human intervention. Need of early worm detection has changed research paradigm from signature based worm detection to the behavioral based detection. To increase effectiveness of proposed solution, in this paper we present mechanism of detection and prevention of worm in distributed fashion. Furthermore, to minimize the worm destruction; upon worm detection we propagate the possible attack aleγt to neighboring nodes in secure and organized manner. Considering worm behavior, our proposed mechanism detects worm cycles and infection chains to detect the sudden change in network performance. And our model neither needs to maintain a huge database of signatures nor needs to have too much computing power, that is why it is very light and simple. So, our proposed scheme is suitable for the ubiquitous environment. Simulation results illustrate better detection and prevention which leads to the reduction of infection rate.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.1
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• pp.193-204
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• 2017

The security of Internet systems critically depends on the capability to keep anti-virus (AV) software up-to-date and maintain high detection accuracy against new malware. However, malware variants evolve so quickly they cannot be detected by conventional signature-based detection. In this paper, we proposed a malware classification method based on sequence patterns generated from the network flow of malware samples. We evaluated our method with 766 malware samples and obtained a classification accuracy of approximately 40.4%. In this study, malicious codes were classified only by network behavior of malicious codes, excluding codes and other characteristics. Therefore, this study is expected to be further developed in the future. Also, we can predict the attack groups and additional attacks can be prevented.

• Journal of the Korea Society of Computer and Information
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• v.25 no.11
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• pp.131-138
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• 2020

Malware is generally recognized as a computer program that penetrates another computer system and causes malicious behavior intended by the developer. In cyberspace, it is also used as a cyber weapon to attack adversary. The most important factor that a malware must have as a cyber weapon is that it must achieve its intended purpose before being detected by the other's detection system. It requires a lot of time and expertise to create a single malware to avoid the other's detection system. We propose the framework that automatically generates variant malware when a binary code type malware is input using the DCM technique. In this framework, the sample malware was automatically converted into variant malware, and it was confirmed that this variant malware was not detected in the signature-based malware detection system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.8B
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• pp.921-928
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• 2011

The increase of Internet users and services has caused the upsurge of data traffic over the network. Nowadays, variety of Internet applications has emerged which generates complicated and diverse data traffic. For the efficient management of Internet traffic, many traffic classification methods have been proposed. But most of the methods focused on the application-level classification, not the function-level classification or state changes of applications. The functional classification of application traffic makes possible the in-detail understanding of application behavior as well as the fine-grained control of applications traffic. In this paper we proposed automata based functional classification method of IM application traffic. We verified the feasibility of the proposed method with function-level control experiment of IM application traffic.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.3
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• pp.563-577
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• 2017

Due to the Internet and computing capability, new and variant malware are discovered around 1 Million per day. Companies use dynamic analysis such as behavior analysis on virtual machines for unknown malware detection because attackers use unknown malware which is not detected by signature based AV effectively. But growing number of malware types are not only PE(Portable Executable) but also non-PE such as MS word or PDF therefore dynamic analysis must need more resources and computing powers to improve detection effectiveness. This study elicits the pre-filtering system evaluation factor to improve effective dynamic malware analysis system and presents and verifies the decision making model and the formula for solution selection using AHP(Analytics Hierarchy Process)

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.5
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• pp.181-195
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• 2011

There's a controversy about an invasion of privacy which includes a leakage of private information and linking of user's behavior on internet. Although many solutions for this problem are proposed, we think anonymous authentication, authorization, and payment mechanism is the best solution for this problem. In this paper, we propose an effective anonymity-based method that achieves not only authentication but also authorization. Our proposed method uses anonymous qualification certificate and group signature method as an underlying primitive, and combines anonymous authentication and qualification information. An eligible user is legitimately issued a group member key pair through key issuing process and issued some qualification certificates anonymously, and then, he can take the safe and convenience web service which supplies anonymous authentication and authorization. The qualification certificate can be expanded according to application environment and it can be used as payment token.

• Journal of Internet Computing and Services
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• v.22 no.2
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• pp.77-87
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• 2021

With the development of the Internet and personal computers, various and complex attacks begin to emerge. As the attacks become more complex, signature-based detection become difficult. It leads to the research on behavior-based log anomaly detection. Recent work utilizes deep learning to learn the order and it shows good performance. Despite its good performance, it does not provide any explanation for prediction. The lack of explanation can occur difficulty of finding contamination of data or the vulnerability of the model itself. As a result, the users lose their reliability of the model. To address this problem, this work proposes an explainable log anomaly detection system. In this study, log parsing is the first to proceed. Afterward, sequential rules are extracted by Bayesian posterior probability. As a result, the "If condition then results, post-probability" type rule set is extracted. If the sample is matched to the ruleset, it is normal, otherwise, it is an anomaly. We utilize HDFS datasets for the experiment, resulting in F1score 92.7% in test dataset.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.3
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• pp.499-510
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• 2023

Recently, an intelligent and advanced cyber attack attacks a computer network of a public institution using a file containing malicious code or leaks information, and the damage is increasing. Even in public institutions with various information protection systems, known attacks can be detected, but unknown dynamic and encryption attacks can be detected when existing signature-based or static analysis-based malware and ransomware file detection methods are used. vulnerable to The detection method proposed in this study extracts the detection result data of the system that can detect malicious code and ransomware among the information protection systems actually used by public institutions, derives various attributes by combining them, and uses a machine learning classification algorithm. Results are derived through experiments on how the derived properties are classified and which properties have a significant effect on the classification result and accuracy improvement. In the experimental results of this paper, although it is different for each algorithm when a specific attribute is included or not, the learning with a specific attribute shows an increase in accuracy, and later detects malicious code and ransomware files and abnormal behavior in the information protection system. It is expected that it can be used for property selection when creating algorithms.