• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.154-156
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• 2020

보안업무를 수행하는 담당자로서 사이버 피해 여부를 파악하기 위한 가장 중요한 업무 중의 하나는 피해를 입은 시스템과 서비스에서 발생되는 다양한 로그들을 정확하게 분석하는 것이다. 그러나 해당 기관이 보안로그를 전문적으로 분석하는 SIEM(Security Information and Event Management)과 같은 솔루션이 없을 경우 보안업무 담당자가 피해 시스템에서 추출된 로그만 가지고 직접 분석하여 공격여부를 판단하기는 쉽지 않다. 따라서 본 논문에서는 정규표현식을 이용하여 다양한 시스템의 로그를 쉽고 정확하게 분석하는 방법을 제시한다.

• Convergence Security Journal
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• v.22 no.2
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• pp.9-19
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• 2022

As the expansion of digital transformation, we are more exposed to the threat of cyber attacks, and many institution or company is operating a signature-based intrusion prevention system at the forefront of the network to prevent the inflow of attacks. However, in order to provide appropriate services to the related ICT system, strict blocking rules cannot be applied, causing many false events and lowering operational efficiency. Therefore, many research projects using artificial intelligence are being performed to improve attack detection accuracy. Most researches were performed using a specific research data set which cannot be seen in real network, so it was impossible to use in the actual system. In this paper, we propose a technique for classifying major attack keywords in the security event log collected from the actual system, assigning a weight to each key keyword, and then performing a similarity check using TF-IDF to determine whether an actual attack has occurred.

• Journal of Internet Computing and Services
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• v.11 no.6
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• pp.73-86
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• 2010

In proportion to the rapid increase in the number of Web users, web attack techniques are also getting more sophisticated. Therefore, we need not only to detect Web attack based on the log analysis but also to extract web attack events from audit information such as Web firewall, Web IDS and system logs for detecting abnormal Web behaviors. In this paper, web attack detection system was designed and implemented based on integrated web audit data for detecting diverse web attack by generating integrated log information generated from W3C form of IIS log and web firewall/IDS log. The proposed system analyzes multiple web sessions and determines its correlation between the sessions and web attack efficiently. Therefore, proposed system has advantages on extracting the latest web attack events efficiently by designing and implementing the multiple web session and log correlation analysis actively.

• Journal of Digital Convergence
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• v.10 no.5
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• pp.223-232
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• 2012

Recently, the leakage of personal information and privacy piracy increase. The victimized case of the malicious object rapidlies increase. Most of users use the windows operating system. Recently, the Windows 7 operating system was announced. Therefore, we need to study for the intrusion response technique at the next generation operate system circumstances. The accident response technique developed till now was mostly implemented around the Windows XP or the Windows Vista. However, a new vulnerability problem will be happen in the breach process of reaction as the Windows 7 operating system is announced. In the windows operating system, the system incident event needs to be efficiently analyzed. For this, the event information generated in a system needs to be visually analyzed around the time information or the security threat weight information. Therefore, in this research, we analyzed visually about the system event information generated in the Windows 7 operating system. And the system analyzing the system incident through the visual event information analysis process was designed and implemented. In case of using the system developed in this study the more efficient accident analysis is expected to be possible.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.5
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• pp.1131-1141
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• 2015

In this paper, we proposed a new approach of machine learning based method for detecting game-bots from normal players in MMORPG by inspecting the player's action log data especially in-game money increasing/decreasing event log data. DBSCAN (Density Based Spatial Clustering of Applications with Noise), an one of density based clustering algorithms, is used to extract the attributes of spatial characteristics of each players such as a number of clusters, a ratio of core points, member points and noise points. Most of all, even game-bot developers know principles of this detection system, they cannot avoid the system because moving a wide area to hunt the monster is very inefficient and unproductive. As the result, game-bots show definite differences from normal players in spatial characteristics such as very low ratio, less than 5%, of noise points while normal player's ratio of noise points is high. In experiments on real action log data of MMORPG, our game-bot detection system shows a good performance with high game-bot detection accuracy.

• Journal of Korea Multimedia Society
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• v.9 no.8
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• pp.1037-1044
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• 2006

It's difficult to check cyber attacks and the performance of a security system in a real large-scale network. Generally, a new security system or the effect of a new security attack are checked by simulation. We use SSFNet to simulate our security system and cyber attack. SSFNet is an event-driven simulation tools based on process, which has a strength to be capable of expressing a large-scale network. But it doesn't offer any API's which can manipulate not only the related function of security but also the packet. In this paper, we developed a firewall and IPS class, used for a security system, and added to them components of SSFNet. The firewall is modelled a security system based on packet filtering. We checked the function of the firewall and the IPS with network modelled as using our SSFNet. The firewall blocks packets through rules of an address and port of packets. The result of this simulation shows that we can check a status of packets through a log screen of IPS installed in a router and confirm abnormal packet to be dropped.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.4
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• pp.797-808
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• 2012

In smart work environment, a company provides employees a flexible work environment for tele-working using mobile phone or portable devices. On the other hand, such environment are exposed to the risks which the attacker can intrude into computer systems or leak personal information of smart-workers' and gain a company's sensitive information. To reduce these risks, the security administrator needs to analyze the usage patterns of employees and detect abnormal behaviors by monitoring VPN(Virtual Private Network) access log. This paper proposes a decision support system that can notify the status by using visualization and similarity measure through clustering analysis. On average, 88.7% of abnormal event can be detected by this proposed method. With this proposed system, the security administrator can detect abnormal behaviors of the employees and prevent account theft.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.2
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• pp.471-484
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• 2018

Recently, cyber threats are frequently occurring in ICS(industrial control systems) of government agencies, infrastructure, and manufacturing companies. In order to cope with such cyber threats, it is necessary to apply CTI to ICS. For this purpose, a security information collection system is needed. However, it is difficult to install security solution in control devices such as PLC. Therefor, it is difficult to collect security information of ICS. In addition, there is a problem that the security information format generated in various assets is different. Therefore, in this paper, we propose an efficient method to collect ICS security information. We utilize CybOX/STIX/TAXII CTI models that are easy to apply to ICS. Using this model, we designed the formats to collect security information of ICS assets. We created formats for system logs, IDS logs, and EWS application logs of ICS assets using Windows and Linux. In addition, we designed and implemented a security information collection system that reflects the designed formats. This system can be used to apply monitoring system and CTI to future ICS.

• Journal of Convergence for Information Technology
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• v.12 no.2
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• pp.47-53
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• 2022

Recently, with the change of the intelligent security paradigm, study to apply various information generated from various information security systems to AI-based anomaly detection is increasing. Therefore, in this study, in order to convert log-like time series data into a vector, which is a numerical feature, the CBOW and Skip-gram inference methods of deep learning-based Word2Vec model and statistical method based on the coincidence frequency were used to transform the published ADFA system call data. In relation to this, an experiment was carried out through conversion into various embedding vectors considering the dimension of vector, the length of sequence, and the window size. In addition, the performance of the embedding methods used as well as the detection performance were compared and evaluated through GRU-based anomaly detection model using vectors generated by the embedding model as an input. Compared to the statistical model, it was confirmed that the Skip-gram maintains more stable performance without biasing a specific window size or sequence length, and is more effective in making each event of sequence data into an embedding vector.