• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.2
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• pp.397-404
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• 2016

Illegal and harmful contents on the Internet has been an issue and been increased in Korea. They are often posted on the billboard and website of small enterprise and government office. Those illegal and harmful contents can relate to crime and suspicious activity, so, we need a detection system. However, to date the detection itself has been conducted manually by a person. In this paper, we develop an automated URL detection scheme for detecting a drug trafficking by using Google. This system works by analyzing the frequently used keywords in a drug trafficking and generate a keyword dictionary to store words for future search. The suspected drug trafficking URL are automatically collected based on the keyword dictionary by using Google search engine. The suspicious URL can be detected by classifying and numbering each domain from the collection of the suspected URL. This proposed automated URL detection can be an effective solution for detecting a drug trafficking, also reducing time and effort consumed by human-based URL detection.

• Journal of the Korea Society of Computer and Information
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• v.25 no.11
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• pp.123-129
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• 2020

Recently, cybercrime has become increasingly difficult to track by applying new technologies such as virtualization technology and distribution tracking avoidance. etc. Therefore, there is a limit to the technology of tracking distributors based on malicious code information through static and dynamic analysis methods. In addition, in the field of cyber investigation, it is more important to track down malicious code distributors than to analyze malicious codes themselves. Accordingly, in this paper, we propose a next-generation malicious code information collection architecture to efficiently track down malicious code distributors by converging traditional analysis methods and recent information collection methods such as OSINT and Intelligence. The architecture we propose in this paper is based on the differences between the existing malicious code analysis system and the investigation point's analysis system, which relates the necessary elemental technologies from the perspective of cybercrime. Thus, the proposed architecture could be a key approach to tracking distributors in cyber criminal investigations.

• Smart Media Journal
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• v.11 no.10
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• pp.46-53
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• 2022

Today, due to the 4th industrial revolution and extensive R&D funding, domestic companies have begun to possess world-class industrial technologies and have grown into important assets. The national government has designated it as a "national core technology" in order to protect companies' critical industrial technologies. Particularly, technology leaks in the shipbuilding, display, and semiconductor industries can result in a significant loss of competitiveness not only at the company level but also at the national level. Every year, there are more insider leaks, ransomware attacks, and attempts to steal industrial technology through industrial spy. The stolen industrial technology is then traded covertly on the dark web. In this paper, we propose a system for detecting industrial technology leaks in the dark web environment. The proposed model first builds a database through dark web crawling using information collected from the OSINT environment. Afterwards, keywords for industrial technology leakage are extracted using the KeyBERT model, and signs of industrial technology leakage in the dark web environment are proposed as quantitative figures. Finally, based on the identified industrial technology leakage sites in the dark web environment, the possibility of secondary leakage is detected through the PageRank algorithm. The proposed method accepted for the collection of 27,317 unique dark web domains and the extraction of 15,028 nuclear energy-related keywords from 100 nuclear power patents. 12 dark web sites identified as a result of detecting secondary leaks based on the highest nuclear leak dark web sites.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.749-766
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• 2021

Cyber powers around the world are conducting cyber information-gathering activities in cyberspace, a global domain within the Internet-based information environment. Accordingly, it is imperative to obtain the latest information through the cyber intelligence preparation of the battlefield (IPB) process to prepare for future cyber operations. Research utilizing the cyber battlefield visualization method for effective cyber IPB and situation awareness aims to minimize uncertainty in the cyber battlefield and enable command control and determination by commanders. This paper designed architecture by classifying cyberspace into a physical, logical network layer and cyber persona layer to visualize the cyber battlefield using BGP archive data, which is comprised of BGP connection information data of routers around the world. To implement the architecture, BGP archive data was analyzed and pre-processed, and cyberspace was implemented in the form of a Di-Graph. Information products that can be obtained through visualization were classified for each layer of the cyberspace, and a visualization method was proposed for performing cyber IPB. Through this, we analyzed actual North Korea's BGP and OSINT data to implement North Korea's cyber battlefield centered on the Internet network in the form of a prototype. In the future, we will implement a prototype architecture based on Elastic Stack.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.2
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• pp.267-280
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• 2023

With development of computing and communications technologies, IoT environments based on high-speed networks have been extending rapidly. Especially, from home to an office or a factory, applications of IoT devices with sensing environment and performing computations are increasing. Unfortunately, IoT devices which have limited hardware resources can be vulnerable to cyber attacks. Hence, there is a concern that an IoT botnet can give rise to information leakage as a national cyber security crisis arising from abuse as a malicious waypoint or propagation through connected networks. In order to response in advance from unknown cyber threats in IoT networks, in this paper, We firstly define four types of We firstly define four types of characteristics by analyzing darknet traffic accessed from an IoT botnet. Using the characteristic, a suspicious IP address is filtered quickly. Secondly, the filtered address is identified by Cyber Threat Intelligence (CTI) or Open Source INTelligence (OSINT) in terms of an unknown suspicious host. The identified IP address is finally fingerprinted to determine whether the IP is a malicious host or not. To verify a validation of the proposed method, we apply to a Darknet on real-world SOC. As a result, about 1,000 hosts who are detected and blocked preemptively by the proposed method are confirmed as real IoT botnets.