• The Journal of the Korea Contents Association
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• v.18 no.11
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• pp.484-492
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• 2018

This study examined the effects of field complexity and type of target objects on the performance in baggage screening task. A total of 62 participants(male: 45.2%, female: 54.8%) participated and their mean age was 22.88. The simulated baggage screening task was developed for this study and after the orientation and task exercises, main experiment session was conducted. Participants performed a total of 200 tasks and 40(20%) contained target object. The complexity was set at three levels: high, middle, and low levels and the number of background items contained 20, 14. and 8 respectively. The type of target was set as gun, knife, liquid, and righter. The dependent variables were hit ratio and reaction time. The results showed that the hit ratio decreased and reaction time increased significantly as field complexity increased, and they varied depending on the type of target. The hit ratio of the knife was the highest and liquid lowest and reaction time of the knife was the fastest and liquid slowest. In addition, the interaction effect was also significant. Knife was not affected by complexity, however, small item such as lighter was most affected by complexity.

• The KIPS Transactions:PartC
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• v.12C no.4 s.100
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• pp.609-616
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• 2005

Signature-based Intrusion Detection has many false positive and many difficulties to detect new and changed attacks. Alpha-cut is introduced which reduces false positive with a combination of signature-based IDS and machine learning-based IDS in prior paper [1]. This research is a study of a succession of Alpha-cut, and we introduce Beta-rick in which attacks can be detected but cannot be detected in single signature-based detection. Alpha-cut is a way of increasing detection accuracy for the signature based IDS, Beta-pick is a way which decreases the case of treating attack as normality. For Alpha-cut and Beta-pick we use XIBL as a learning algorithm and also show the difference of result of Sd.5. To describe the value of proposed method we apply Alpha-cut and Beta-pick to signature-based IDS and show the decrease of false alarms.

• The KIPS Transactions:PartC
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• v.12C no.2 s.98
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• pp.191-200
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• 2005

The brilliant achievements in computers and the internet technology make it easy for users to get useful information. But at the same time, the damages caused by intrusions and denial of service attacks are getting more worse. Specially because denial of service attacks by internet worm incapacitate computers and networks, we should draw up a disposal plan against it. So far many rule-based intrusion detection systems have been developed, but these have the limits of these ability to detect new internet worms. In this paper, we propose a system to detect intrusion and generate detection rule against scan-based internet worm, paying attention to the fact that internet worms scan network to infect hosts. The system detects internet worms using detection rule. And if it detects traffic causing by a new scan-based internet worm, it generates new detection nile using traffic information that is gathered. Therefore it can response to new internet worms early. Because the system gathers packet payload, when it is being necessary only, it can reduce system's overhead and disk space that is required.

• Journal of the Korean Magnetics Society
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• v.26 no.2
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• pp.62-66
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• 2016

We designed a model composed a ring type magnet, a yoke, and a sensing coil buried in a projectile for calculating the muzzle velocity based on the voltage induced from sensing coil by simulation. The muzzle velocity was calculated from the master curve obtained through the voltage induced from sensing coil by simulation. The induced voltage increased with increasing the diameter of sensing coil. The projectile's velocity was proportional to the induced voltage when the sensing coil was buried in projectile. The projectile will be surely exploded at the target region by inputting the information of muzzle velocity variation corrected the diameter effect of induced voltage of sensing coil.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.5
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• pp.630-636
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• 2020

This study reviewed camouflage techniques to reduce the visual detect-ability of aircraft. Visual camouflage can be defined as the process of making objects less visible. Aircraft visual camouflage delays detection of the aircraft position, speed, and flight direction. Multi-tone and counter-shaded schemes are generally adopted as camouflage patterns for close-air-support aircraft and air-superiority aircraft, respectively. Another study showed that the monotone scheme is also efficient when the hue and brightness of the camouflage color are controlled correctly. Active camouflage techniques for aircraft have been studied to increase the camouflage effectiveness. In particular, counter-illumination techniques using electroluminescence devices can minimize the difference in brightness between the aircraft and sky background. Active camouflage techniques are expected to enhance the survivability of low-altitude UAVs, which are vulnerable to visual detection.

• The KIPS Transactions:PartC
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• v.10C no.6
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• pp.705-710
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• 2003

Recently, most interchanges of information have been performed in the internet environments. So, the technuque, which is used as intrusion deleting tool for system protecting against attack, is very important. But, the skills of intrusion detection are newer and more delicate, we need preparations for defending from these attacks. Currently, lots of intrusion detection systemsmake the midel of intrusion detection rule using experienced data, based on this model they have the strategy of defence against attacks. This is not efficient for defense from new attack. In this paper, a new model of intrusion detection is proposed. This is hybrid statistical learning model using likelihood ratio test and statistical learning theory, then this model can detect a new attack as well as experienced attacks. This strategy performs intrusion detection according to make a model by finding abnomal attacks. Using KDD Cup-99 task data, we can know that the proposed model has a good result of intrusion detection.

• The Journal of Society for e-Business Studies
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• v.26 no.3
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• pp.21-32
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• 2021

Due to the recent development in electronic financial services, transactions of electronic prepayment are rapidly increasing. The increased transactions of electronic prepayment, however, also leads to the increased fraud attempts. It is mainly because electronic prepayment can easily be converted into cash. The objective of this paper is to develop a methodology that can effectively detect fraud transactions in electronic prepayment, by using sequential pattern mining techniques. To validate our approach, experiments on real transaction data were conducted and the applicability of the proposed method was demonstrated. As a result, the accuracy of the proposed method has been 95.6 percent, showing that the proposed method can effectively detect fraud transactions. The proposed method could be used to reduce the damage caused by the fraud attempts of electronic prepayment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.4
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• pp.445-452
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• 2019

Today, most detection systems used in the marine industry are the majority of devices operating in the high-power X-band bands. While most detection systems using these frequencies in the X-Band band can expect a wide range of detection performance, they are not suitable for precision detection and have the limitation that they are large and heavy. In this paper, we designed, fabricated and tested an electronic scanner capable of detecting not only the surrounding objects but also the ocean waves at a low power of less than 2W in the 17GHz frequency band of the Ku-Band. A high-performance patch array antenna and Doppler effect were utilized to obtain sufficient detection performance even at low power. As a result of the test, it was confirmed that the performance was sufficiently valuable.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.4
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• pp.785-796
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• 2012

In this paper, we propose an effective Behavior-based detection technique using the frequency of system calls to detect malicious code, when the number of training data is fewer than the number of properties on system calls. In this study, we collect the Native APIs which are Windows kernel data generated by running program code. Then we adopt the normalized freqeuncy of Native APIs as the basic properties. In addition, the basic properties are transformed to new properties by GLDA(Generalized Linear Discriminant Analysis) that is an effective method to discriminate between malicious code and normal code, although the number of training data is fewer than the number of properties. To detect the malicious code, kNN(k-Nearest Neighbor) classification, one of the bayesian classification technique, was used in this paper. We compared the proposed detection method with the other methods on collected Native APIs to verify efficiency of proposed method. It is presented that proposed detection method has a lower false positive rate than other methods on the threshold value when detection rate is 100%.

• Convergence Security Journal
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• v.1 no.1
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• pp.21-29
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• 2001

For multiple attacks through large networks e.g., internet, IDS had better be installed over several hosts and collect all the audit data from them with appropriate synthesis. We propose a new distributed intrusion detection system called SPIDER II which is the upgraded version of the previous standalone IDS - SPIDER I. As like the previous version, SPIDER II has been implemented on Linux Accel 6.1 in CNU C. After planting intrusion detection engines over several target hosts as active agents, the administration module of SPIDER II receives all the logs from agents and analyzes hem. For the world-wide standardization on IDS, SPIDER II is compatible with MITRE's CVE(Common Vulnerabilities and Exposures).