• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.5
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• pp.1137-1143
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• 2015

Sensitive internal information has been transmitted in a variety of services of Internet environment, but almost users do not know what internal information is sent. In this paper, we intend to develop a new filtering system that continuously monitors the sensitive information in outbound network packets and notifies the internal user whether or not to expose. So we design a filtering system for sensitive information and analyze the implementation results. Thus users visually can check whether disclosure of the important information and drop the corresponding packets by the proposed system. The results of this study can help decrease cyber threats various targeting internal information of company by contributing to prevent exposure of sensitive internal information.

• Journal of the Korea Society for Simulation
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• v.19 no.4
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• pp.151-159
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• 2010

Currently, by using the Internet, We can do varius things such as Web surfing, email, on-line shopping, stock trading on your home or office. However, as being out of the concept of security from the beginning, it is the big social issues that malicious user intrudes into the system through the network, on purpose to steal personal information or to paralyze system. In addition, network intrusion by ordinary people using network attack tools is bringing about big worries, so that the need for effective and powerful intrusion detection system becomes very important issue in our Internet environment. However, it is very difficult to prevent this attack perfectly. In this paper we proposed the algorithm for the detection of DoS attacks, and developed attack detection tools. Through learning in a normal state on Step 1, we calculate thresholds, the number of packets that are coming to each port, the median and the average utilization of each port on Step 2. And we propose values to determine how to attack detection on Step 3. By programing proposed attack detection algorithm and by testing the results, we can see that the difference between the median of packet mounts for unit interval and the average utilization of each port number is effective in detecting attacks. Also, without the need to look into the network data, we can easily be implemented by only using the number of packets to detect attacks.