• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.22 no.2
• /
• pp.283-293
• /
• 2012

A new type of malware that extracts personal and account data over an extended period of time and that apparently is resistant to detection by vaccines has been identified. Generally, a malware is installed on a computer through network-to-network connections by utilizing Web vulnerabilities that contain injection, XSS, broken authentication and session management, or insecure direct-object references, among others. After the malware executes registration of an arbitrary service and an arbitrary process on a computer, it then periodically communicates the collected confidential information to a hacker. This paper is a systematic approach to analyzing a new type of malware called "winweng," a kind of worm that frequently made appearances during the first half of 2011. The research describes how the malware came to be in circulation, how it infects computers, how its operations expose its existence and suggests improvements in responses and countermeasures. Keywords: Malware, Worm, Winweng, SNORT.

• Journal of Digital Convergence
• /
• v.11 no.4
• /
• pp.215-220
• /
• 2013

The super computer calls usually as the super computer in case the computing power of the computer is 20 G flops (GFLOPS) or greater. In the past, the computer equipped with the vector processor (the instrument processing the order having the logic operation and maximum value or minimum value besides the common computer instruction) processing the scientific calculation with the super high speed was installed as the super computer. Recently, cyber attack focuses on supercomputer because if it is being infected, then it will affect hundreds of client PC. Therefore, our research paper analyzed super computer security issues and biometric countermeasure to develop the level of security on super computer.

• The Journal of the Korea Contents Association
• /
• v.10 no.5
• /
• pp.45-51
• /
• 2010

Appearance of a cloned AP(Access Point) causes MS(Mobile Station) to break an association with a normal AP(Access Point). If signal power of the cloned AP is stronger than that of the normal AP, MS associates with the cloned AP. Therefore, MS is easily exposed to attackers who installed the cloned AP. In this paper, we distinguish cloned AP from normal AP by using the association time and frame sequence number between normal AP and MS, then isolates the cloned AP. The simulation by NS-2 shows that our mechanism isolates efficiently a cloned AP and builds safer wireless LAN environment.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2009.11a
• /
• pp.655-656
• /
• 2009

인터넷전화는 저렴한 가격과 다양한 서비스를 장점과 번호이동과 같은 정부 정책에 힘입어 급속한 성장을 이루고있다. SIP는 VoIP의 시그널링 프로토콜로서 다양한 부가서비스 제공을 위해 기존의 H.323을 대체하고 있을뿐만 아니라, IMS의 시그널링 제어 프로토콜로 채택되어 향후 SIP기반 응용서비스의 급속한 성장이 예상된다. 하지만 SIP기반 응용서비스는 기존의 IP 망에서 발생되는 보안 위협뿐만 아니라 SIP 특성에 기인한 신규 위협에 의한 피해 사례가 발생하고 있음에도 불구하고, 현재 보안 시스템으로는 이러한 위협에 효과적으로 대응하지 못하고 있다. 본 논문에서는 SIP기반 네트워크 보안 위협에 대해서 설명하고, 이러한 공격을 탐지 및 대응할 수 있는 SIP-Aware 침입방지시스템 설계 및 구조도를 설명한다.

• Journal of the Korean Data and Information Science Society
• /
• v.22 no.6
• /
• pp.1053-1063
• /
• 2011

Social network analysis is the social statistical analysis of any social structure involving a stream of mutual information between observations. In this study we used the results of passes between players in a soccer game. The analysis contents are as follows. (1) Players with important or leading roles are identified. (2) Players are assessed by pass frequency and the success rate of passes. The purpose of this study is for use as basic data for future team strategy, and achieves this by evaluating the role of each individual player within a team. In this study, social network analysis without separating positions is conducted, and is also performed for defensive and attacking positions respectively. The results of this study are as follows: First, when complete team data were available, the players performing leadership roles were Jung-woo Kim, Sung-yeung Ki and Chung-young Lee, whereas Jeong-su Lee acted as a sub-leader. In case of data for defensive positions Jeong-su Lee was a leading player, and in terms of attacking positions, all of the players excelled in the game and could be evaluated as playing lead roles.

• Journal of Internet Computing and Services
• /
• v.23 no.6
• /
• pp.39-48
• /
• 2022

Cyberattacks around the world continue to increase, and their damage extends beyond government facilities and affects civilians. These issues emphasized the importance of developing a system that can identify and detect cyber anomalies early. As above, in order to effectively identify cyber anomalies, several studies have been conducted to learn BGP (Border Gateway Protocol) data through a machine learning model and identify them as anomalies. However, BGP data is unbalanced data in which abnormal data is less than normal data. This causes the model to have a learning biased result, reducing the reliability of the result. In addition, there is a limit in that security personnel cannot recognize the cyber situation as a typical result of machine learning in an actual cyber situation. Therefore, in this paper, we investigate BGP (Border Gateway Protocol) that keeps network records around the world and solve the problem of unbalanced data by using SMOTE. After that, assuming a cyber range situation, an autoencoder classifies cyber anomalies and visualizes the classified data. By learning the pattern of normal data, the performance of classifying abnormal data with 92.4% accuracy was derived, and the auxiliary index also showed 90% performance, ensuring reliability of the results. In addition, it is expected to be able to effectively defend against cyber attacks because it is possible to effectively recognize the situation by visualizing the congested cyber space.

• Journal of Korea Society of Industrial Information Systems
• /
• v.17 no.7
• /
• pp.51-57
• /
• 2012

This paper proposes a group key design based on ECDH(Elliptic Curve Diffie Hellman) which guarantees secure V2V and V2I communication. The group key based on ECDH generates the VGK(Vehicular Group key) which is a group key between vehicles, the GGK(Global Group Key) which is a group key between vehicle groups, and the VRGK(Vehicular and RSU Group key) which is a group key between vehicle and RSUs with ECDH algorithm without an AAA server being used. As the VRGK encrypted with RGK(RSU Group Key) is transferred from the current RSU to the next RSU through a secure channel, a perfect forward secret security is provided. In addition, a Sybil attack is detected by checking whether the vehicular that transferred a message is a member of the group with a group key. And the transmission time of messages and the overhead of a server can be reduced because an unnecessary network traffic doesn't happen by means of the secure communication between groups.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2017.04a
• /
• pp.315-318
• /
• 2017

Nowadays, Distributed Denial of Service (DDoS) attacks have gained increasing popularity and have been a major factor in a number of massive cyber-attacks. It could easily exhaust the computing and communicating resources of a victim within a short period of time. Therefore, we have to find the method to detect and prevent the DDoS attack. Recently, there have been some researches that provide the methods to resolve above problem, but it still gets some limitations such as low performance of detecting and preventing, scope of method, most of them just use on cloud server instead of network, and the reliability in the network. In this paper, we propose solutions for (1) handling multiple DDoS attacks from multiple IP address and (2) handling the suspicious attacks in the network. For the first solution, we assume that there are multiple attacks from many sources at a times, it should be handled to avoid the conflict when we setup the preventing rule to switches. In the other, there are many attacks traffic with the low volume and same destination address. Although the traffic at each node is not much, the traffic at the destination is much more. So it is hard to detect that suspicious traffic with the sampling based method at each node, our method reroute the traffic to another server and make the analysis to check it deeply.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.12
• /
• pp.1872-1879
• /
• 2022

Recently, with the development of artificial intelligence technology, research to use artificial intelligence to detect hacking attacks is being actively conducted. However, the fact that security data is a representative imbalanced data is recognized as a major obstacle in composing the learning data, which is the key to the development of artificial intelligence models. Therefore, in this paper, we propose a W-VAE oversampling technique that applies VAE, a deep learning generation model, to data extraction for oversampling, and sets the number of oversampling for each class through weight calculation using K-NN for sampling. In this paper, a total of five oversampling techniques such as ROS, SMOTE, and ADASYN were applied through NSL-KDD, an open network security dataset. The oversampling method proposed in this paper proved to be the most effective sampling method compared to the existing oversampling method through the F1-Score evaluation index.

• Convergence Security Journal
• /
• v.21 no.2
• /
• pp.11-18
• /
• 2021

To protect tangible and intangible assets, most of the companies are conducting information protection monitoring by using various security equipment in the IT service network. As the security equipment that needs to be protected increases in the process of upgrading and expanding the service network, it is difficult to monitor the possible exposure to the attack for the entire service network. As a countermeasure to this, various studies have been conducted to detect external attacks and illegal communication of equipment, but studies on effective monitoring of the open service ports and construction of illegal communication monitoring system for large-scale service networks are insufficient. In this study, we propose a framework that can monitor information leakage and illegal communication attempts in a wide range of service networks without large-scale investment by analyzing 'Netflow statistical information' of backbone network equipment, which is the gateway to the entire data flow of the IT service network. By using machine learning algorithms to the Netfllow data, we could obtain the high classification accuracy of 94% in identifying whether the Telnet service port of operating equipment is open or not, and we could track the illegal communication of the damaged equipment by using the illegal communication history of the damaged equipment.