• The Journal of the Korea Contents Association
• /
• v.21 no.1
• /
• pp.531-540
• /
• 2021

As cyber attacks and crimes increase exponentially and hacking attacks become more intelligent and advanced, hacking attack methods and routes are evolving unpredictably and in real time. In order to reinforce the enemy's responsiveness, this study aims to propose a method for developing an artificial intelligence-based security control platform by building a next-generation security system using artificial intelligence to respond by self-learning, monitoring abnormal signs and blocking attacks.The artificial intelligence-based security control platform should be developed as the basis for data collection, data analysis, next-generation security system operation, and security system management. Big data base and control system, data collection step through external threat information, data analysis step of pre-processing and formalizing the collected data to perform positive/false detection and abnormal behavior analysis through deep learning-based algorithm, and analyzed data Through the operation of a security system of prevention, control, response, analysis, and organic circulation structure, the next generation security system to increase the scope and speed of handling new threats and to reinforce the identification of normal and abnormal behaviors, and management of the security threat response system, Harmful IP management, detection policy management, security business legal system management. Through this, we are trying to find a way to comprehensively analyze vast amounts of data and to respond preemptively in a short time.

• Journal of KIISE:Computing Practices and Letters
• /
• v.16 no.4
• /
• pp.405-414
• /
• 2010

According to the statistics of SecurityFocus in 2008, client-side attacks through the Microsoft Internet Explorer have increased by more than 50%. In this paper, we have implemented a behavior-based malicious web page detection system and a blacklist-based malicious web page filtering system. To do this, we first efficiently collected the target URLs by constructing a crawling system. The malicious URL detection system, run on a specific server, visits and renders actively the collected web pages under virtual machine environment. To detect whether each web page is malicious or not, the system state changes of the virtual machine are checked after rendering the page. If abnormal state changes are detected, we conclude the rendered web page is malicious, and insert it into the blacklist of malicious web pages. The malicious URL filtering system, run on the web client machine, filters malicious web pages based on the blacklist when a user visits web sites. We have enhanced system performance by automatically handling message boxes at the time of ULR analysis on the detection system. Experimental results show that the game sites contain up to three times more malicious pages than the other sites, and many attacks incur a file creation and a registry key modification.

• Journal of Internet Computing and Services
• /
• v.5 no.5
• /
• pp.93-103
• /
• 2004

The main function of the intrusion Detection System (IDS) usee to be more or less passive detection of the intrusion evidences, but recently it is developed with more diverse types and methodologies. Especially, it is required that the IDS should process large system audit data fast enough. Therefore the data mining or neural net algorithm is being focused on, since they could satisfy those situations. In this study, we first surveyed and analyzed the several recent intrusion trends and types. And then we designed and implemented an IDS using back-propagation algorithm of the neural net, which could provide more effective solution. The distinctive feature of our study could be stated as follows. First, we designed the system that allows both the Anomaly dection and the Misuse detection. Second, we carried out the intrusion analysis experiment by using the reliable KDD Cup ‘99 data, which would provide us similar results compared to the real data. Finally, we designed the system based on the object-oriented concept, which could adapt to the other algorithms easily.

• Convergence Security Journal
• /
• v.18 no.5_1
• /
• pp.113-120
• /
• 2018

Today, the development of information and communication technology is rapidly increasing the number of users of network-based service, and enables real-time information sharing among users on the Internet. There are various methods in the information sharing process, and information sharing based on portal service is generally used. However, the process of information sharing serves as a cause of illegal activities in order to amplify the social interest of the relevant stakeholders. Public opinion attack using macro function can distort normal public opinion, so security measures are urgent. Therefore, security measures are urgently needed. Macro attacks are generally defined as attacks in which illegal users acquire multiple IP or ID to manipulate public opinion on the content of a particular web page. In this paper, we analyze network path information based on traceback for macro attack of a specific user, and then detect multiple access of the user. This is a macro attack when the access path information for a specific web page and the user information are matched more than once. In addition, when multiple ID is accessed for a specific web page in the same region, it is not possible to distort the overall public opinion on a specific web page by analyzing the threshold count value.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.30 no.3
• /
• pp.465-479
• /
• 2020

Regardless of the domestic and foreign governments/companies, SOC (Security Operation Center) has operated 24 hours a day for the entire year to ensure the security for their IT infrastructures. However, almost all SOCs have a critical limitation by nature, caused from heavily depending on the manual analysis of human agents with the text-based monitoring architecture. Even though, in order to overcome the drawback, technologies for a comprehensive visualization against complex cyber threats have been studying, most of them are inappropriate for the security monitoring in large-scale networks. In this paper, to solve the problem, we propose a novel visual approach for intuitive threats monitoring b detecting suspicious IP address, which is an ultimate challenge in cyber security monitoring. The approach particularly makes it possible to detect, trace and analysis of suspicious IPs statistically in real-time manner. As a result, the system implemented by the proposed method is suitably applied and utilized to the real-would environment. Moreover, the usability of the approach is verified by successful detecting and analyzing various attack IPs.

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

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.16 no.10
• /
• pp.7211-7218
• /
• 2015

Tens of thousands of malicious codes are generated on average in a day. New types of malicious codes are surging each year. Diverse methods are used to detect such codes including those based on signature, API flow, strings, etc. But most of them are limited in detecting new malicious codes due to bypass techniques. Therefore, a lot of researches have been performed for more efficient detection of malicious codes. Of them, visualization technique is one of the most actively researched areas these days. Since the method enables more intuitive recognition of malicious codes, it is useful in detecting and examining a large number of malicious codes efficiently. In this paper, we analyze the relationships between malicious codes and Native API functions. Also, by applying the word cloud with text mining technique, major Native APIs of malicious codes are visualized to assess their maliciousness. The proposed malicious code analysis method would be helpful in intuitively probing behaviors of malware.

• The Journal of Korean Association of Computer Education
• /
• v.13 no.6
• /
• pp.91-98
• /
• 2010

NAC(Network Access Control) has been developed as a solution for the security of end-point user, to be a target computer of worm attack which does not use security patch of OS and install Anti-Virus, which spreads the viruses in the Intra-net. Currently the NAC products in market have a sufficient technology of pre-connect, but insufficient one of post-connect which detects the threats after the connect through regular authentication. Therefore NAC users have been suffered from Zero-day attacks and malware infection. In this paper, to solve the problems in the post-connect step we generate the normal behavior profiles using the traffic information of each host, host information through agent, information of open port and network configuration modification through network scanner addition to authentication of host and inspection of policy violation used before. Based on these we propose the system to detect the hosts infected malware.

• The Journal of the Korea Contents Association
• /
• v.19 no.1
• /
• pp.108-116
• /
• 2019

In this paper, we propose a method to apply artificial intelligence technology efficiently to integrated security control technology. In other words, by applying machine learning learning to artificial intelligence based on big data collected in integrated security control system, cyber attacks are detected and appropriately responded. As technology develops, many large capacity Is limited to analyzing individual logs. The analysis method should also be applied to the integrated security control more quickly because it needs to correlate the logs of various heterogeneous security devices rather than one log. We have newly proposed an integrated security service model based on artificial intelligence, which analyzes and responds to these behaviors gradually evolves and matures through effective learning methods. We sought a solution to the key problems expected in the proposed model. And we developed a learning method based on normal behavior based learning model to strengthen the response ability against unidentified abnormal behavior threat. In addition, future research directions for security management that can efficiently support analysis and correspondence of security personnel through proposed security service model are suggested.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.28 no.2
• /
• pp.357-367
• /
• 2018

As information technology of modern society develops, various malicious codes with the purpose of seizing or destroying important system information are developing together. Among them, ransomware is a typical malicious code that prevents access to user's resources. Although researches on detecting ransomware performing encryption have been conducted a lot in recent years, no additional methods have been proposed to recover damaged files after an attack. Also, because the similarity comparison technique was used without considering the repeated encryption, it is highly likely to be recognized as a normal behavior. Therefore, this paper implements a filter driver to control the file system and performs a similarity comparison method that is verified based on the analysis of the encryption pattern of the ransomware. We propose a system to detect the malicious process of the accessed process and recover the damaged file based on the cloud storage.