• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.48 no.3
• /
• pp.91-101
• /
• 2011

Internet is becoming increasingly popular due to the rapid development of information and communication technology. There has been a convenient social activities such as the mutual exchange of information, e-commerce, internet banking, etc. through cyberspace on a computer. However, by using the convenience of the internet, the personal IDs(identity card, driving license, passport, student ID, etc.) represented by the electronic media are exposed on the internet frequently. Therefore, this study propose a feature extraction method to analyze the characteristics of image files containing personal information and a image retrieval method to find the images using the extracted features. The proposed method selects the feature information from color, texture, and shape of the images, and the images as searched by similarity analysis between feature information. The result which it experiments from the image which it acquires from the web-based image DB and correct image retrieval rate is 89%, the computing time per frame is 0.17 seconds. The proposed method can be efficiently apply a system to search the image files containing personal information and to determine the criteria of exposure of personal information.

• Journal of Digital Contents Society
• /
• v.18 no.7
• /
• pp.1403-1409
• /
• 2017

As the Internet evolves, security becomes more important. Especially, e-mail has become one of the most important services that companies and ordinary users use on the Internet. However, security vulnerabilities such as sniffing attacks, IDs, and password spoofs are causing many problems. This paper introduces an example of implementation of encrypted mailing program with which the secured mail is encrypted by symmetric key methode and the encrypted message can not be read without proper decryption. In order to use the current mailing systems, we keep the rules related to SMTP and POP3, and only the encrypted message is stored in the mail server system and the message can be decrypted only at the terminals of the senders and the receivers with the key which is shared in advanced by independent route between them. This implementation scheme can provide an efficiency that it does not request any change of current mailing system, which can be an additional security protection.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2004.11a
• /
• pp.163-166
• /
• 2004

모바일향 90nm DRAM을 개발하기 위하여 비대칭 채널 구조를 갖는 Recess Channel Array Transistor (RCAT)로 cell transistor를 구현하였다. DRAM cell transistor에서 junction leakage current 증가는 DRAM retention time 열화에 심각한 영향을 미치는 요인으로 알려져 있으며, DRAM의 minimum feature size가 점점 감소함에 따라 short channel effect의 영향으로 junction leakage current는 더욱 더 증가하게 된다. 본 실험에서는 short channel effect의 영향에 의한 junction leakage current를 감소시키기 위하여 Recess Channel Array Transistor를 도입하였고, cell transistor의 채널 영역을 비대칭으로 형성하여 data retention time을 증가시켰다. 비대칭 채널 구조을 이용하여 Recess Channel Array Transistor를 구현한 결과, sub-threshold 특성과 문턱전압, Body effect, 그리고, GIDL 특성에는 큰 유의차가 보이지 않았고, I-V특성인 드레인 포화전류(IDS)는 대칭 채널 구조인 transistor 대비 24.8% 정도 증가하였다. 그리고, data retention time은 2배 정도 증가하였다. 본 실험에서 얻은 결과는 향후 저전압 DRAM 개발과 응용에 상당한 기여를 할 것으로 기대된다.

• The KIPS Transactions:PartC
• /
• v.11C no.1
• /
• pp.39-46
• /
• 2004

It has been happening to transfer not only the general information but also the valuable information through the universal Internet. So security accidents as the expose of secret data and document increase. But we don't have stable structure for transmitting important data. Accordingly, in this paper we intend to use network based Intrusion Detection System modules and detect the extrusion of important data through the network, and propose and design the method for investigating concealment data to protect important data and investigate the secret document against the terrorism. We analyze the method for investigating concealment data, especially we use existing steganalysis techniques, so we propose and design the module emphasizing on the method for investigating stego-data in E-mail of attach files or Web-data of JPG, WAVE etc. Besides, we analyze the outcome through the experiment of the proposed stego-data detection system.

• Journal of the Korea Society of Computer and Information
• /
• v.25 no.2
• /
• pp.49-58
• /
• 2020

In this study, we solve an online multi-object problem which finds object states (i.e. locations and sizes) while conserving their identifications in online-provided images and detections. We handle this problem based on a tracking-by-detection approach by linking (or associating) detections between frames. For more accurate online association, we propose novel online appearance learning with discrete fourier transform and partial least square analysis (PLS). We first transform each object image into a Fourier image in order to extract meaningful features on a frequency domain. We then learn PLS subspaces which can discriminate frequency features of different objects. In addition, we incorporate the proposed appearance learning into the recent confidence-based association method, and extensively compare our methods with the state-of-the-art methods on MOT benchmark challenge datasets.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.19 no.1
• /
• pp.93-101
• /
• 2009

This paper proposes the detection method of spreading mails which hacker injects malicious codes to steal the information. And I developed the 'Analysis model' which is decoding traffics when hacker's encoding them to steal the information. I researched 'Methodology of intrusion detection techniques' in the computer network monitoring. As a result of this simulation, I developed more efficient rules to detect the PCs which are infected malicious codes in the hacking mail. By proposing this security policy which can be applicable in the computer network environment including every government or company, I want to be helpful to minimize the damage by hacking mail with malicious codes.

• International Journal of Computer Science & Network Security
• /
• v.23 no.1
• /
• pp.46-52
• /
• 2023

With billions of IoT (Internet of Things) devices populating various emerging applications across the world, detecting anomalies on these devices has become incredibly important. Advanced Intrusion Detection Systems (IDS) are trained to detect abnormal network traffic, and Machine Learning (ML) algorithms are used to create detection models. In this paper, the NSL-KDD dataset was adopted to comparatively study the performance and efficiency of IoT anomaly detection models. The dataset was developed for various research purposes and is especially useful for anomaly detection. This data was used with typical machine learning algorithms including eXtreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), and Deep Convolutional Neural Networks (DCNN) to identify and classify any anomalies present within the IoT applications. Our research results show that the XGBoost algorithm outperformed both the SVM and DCNN algorithms achieving the highest accuracy. In our research, each algorithm was assessed based on accuracy, precision, recall, and F1 score. Furthermore, we obtained interesting results on the execution time taken for each algorithm when running the anomaly detection. Precisely, the XGBoost algorithm was 425.53% faster when compared to the SVM algorithm and 2,075.49% faster than the DCNN algorithm. According to our experimental testing, XGBoost is the most accurate and efficient method.

• The Journal of the Korean Society for Microbiology
• /
• v.22 no.1
• /
• pp.35-53
• /
• 1987

Multiply resistant Shigella strains isolated in Taegu area were subjected for the characterization of R plasmids. All strains isolated in 1984 and 1985 were susceptible to gentamicin, amikacin, and cephalothin, and most strains were susceptible to kanamycin (Km) and rifampin by agar dilution antimicrobial susceptibility test. The resistance frequency of S. flexneri against ampicillin (Ap) was higher than that of S. sonnei. The strains resistant to sulfisomidine (Su) and trimethoprim (Tp) were found at higher frequency in S. sonnei than in S. flexneri. The most prevalent resistance pattern of S. flexneri was chloramphenicol (Cm) tetracycline (Tc) streptomycin (Sm) Ap, followed by the pattern of CmTcSmSuApTp, CmTcSmSuApTp nalidixic acid, and CmTcSmSuAp in the decreasing order. The antibiogram of CmTcSmSuTp was found to be the most frequent pattern in S. sonnei. The ratio of conjugal transfer of S. flexneri was 47% and 75% of S. sonnei. The average number of plasmid harboring in Shigella was 4 and the size of plasmid ranged 1.3 to 134 megadalton (Mdal). Most S. flexneri carried plasmids of 2 to 3 Mdal and S. sonnei carried those of 3 to 4 Mdal size. The sizes of conjugative plasmids ranged 40-90 Mdal. The incompatibility group (Inc) F II plasmids (54-59 Mdal) were most frequent and rare Inc B plasmids (60 Mdal) of isolates in 1979 and 1980 and Inc FI (87 Mdal) of 1983 isolates were able to be classified by the colony test with standard reference plasmids. The R plasmids of known Inc group were tested for the restriction endonuclease analysis. The pattern of plasmids digested by EcoRl were apparently different by the Inc group but there was no significant difference between species or by the resistance patterns. Nonconjugative plasmids and their phenotypes were identified by transformation test. The transformants were resistant to less than two drugs. Colicin producing transformants carried the Col plasmid of 3.7 or 3.9 Mdal size. $Ap^r$ plasmids derived from S. sonnei were found to be mobilized by transfer factor RT641 to E. coli #CS100. $Ap^r$ plasm ids of same size shared by S. flexneri, S. sonnei, and E. coli were digested with Pstl. All of them showed two restriction fragments of 2.8 kilobase(kb) and 0.7kb. Other plasmids ($Sm^r\;Su^r$) derived from S. flexneri, S. boydii, and S. sonnei were digested with Pstl and they showed same restriction fragment patterns of 3.1kb and 2.9kb. The plasmid profiles of three strains of S. sonnei producing colicin and showing same resistance pattern of CmTcSmSuApTpKm appeared to be similar. Restriction patterns by EcoRl and the behavior of plasmids in conjugation or transformation process were also similar between those plasmids. The restriction patterns were significantly different between the plasmids of Inc FI group and those of unclassified Inc group.

• Journal of Intelligence and Information Systems
• /
• v.18 no.1
• /
• pp.125-141
• /
• 2012

These days, the malicious attacks and hacks on the networked systems are dramatically increasing, and the patterns of them are changing rapidly. Consequently, it becomes more important to appropriately handle these malicious attacks and hacks, and there exist sufficient interests and demand in effective network security systems just like intrusion detection systems. Intrusion detection systems are the network security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. Conventional intrusion detection systems have generally been designed using the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. However, they cannot handle new or unknown patterns of the network attacks, although they perform very well under the normal situation. As a result, recent studies on intrusion detection systems use artificial intelligence techniques, which can proactively respond to the unknown threats. For a long time, researchers have adopted and tested various kinds of artificial intelligence techniques such as artificial neural networks, decision trees, and support vector machines to detect intrusions on the network. However, most of them have just applied these techniques singularly, even though combining the techniques may lead to better detection. With this reason, we propose a new integrated model for intrusion detection. Our model is designed to combine prediction results of four different binary classification models-logistic regression (LOGIT), decision trees (DT), artificial neural networks (ANN), and support vector machines (SVM), which may be complementary to each other. As a tool for finding optimal combining weights, genetic algorithms (GA) are used. Our proposed model is designed to be built in two steps. At the first step, the optimal integration model whose prediction error (i.e. erroneous classification rate) is the least is generated. After that, in the second step, it explores the optimal classification threshold for determining intrusions, which minimizes the total misclassification cost. To calculate the total misclassification cost of intrusion detection system, we need to understand its asymmetric error cost scheme. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, total misclassification cost is more affected by FNE rather than FPE. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 10,000 samples from them by using random sampling method. Also, we compared the results from our model with the results from single techniques to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell R4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on GA outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that the proposed model outperformed all the other comparative models in the total misclassification cost perspective. Consequently, it is expected that our study may contribute to build cost-effective intelligent intrusion detection systems.

• Journal of KIISE:Databases
• /
• v.28 no.4
• /
• pp.529-544
• /
• 2001

Prefetching is an effective method to minimize the number of roundtrips between the client and the server in database management systems. In this paper we propose new notions of the type-level access pattern and the type-level access locality and developed an efficient prefetchin policy based on the notions. The type-level access patterns is a sequence of attributes that are referenced in accessing the objects: the type-level access locality a phenomenon that regular and repetitive type-level access patterns exist. Existing prefetching methods are based on object-level or page-level access patterns, which consist of object0ids of page-ids of the objects accessed. However, the drawback of these methods is that they work only when exactly the same objects or pages are accessed repeatedly. In contrast, even though the same objects are not accessed repeatedly, our technique effectively prefetches objects if the same attributes are referenced repeatedly, i,e of there is type-level access locality. Many navigational applications in Object-Relational Database Management System(ORDBMs) have type-level access locality. Therefore our technique can be employed in ORDBMs to effectively reduce the number of roundtrips thereby significantly enhancing the performance. We have conducted extensive experiments in a prototype ORDBMS to show the effectiveness of our algorithm. Experimental results using the 007 benchmark and a real GIS application show that our technique provides orders of magnitude improvements in the roundtrips and several factors of improvements in overall performance over on-demand fetching and context-based prefetching, which a state-of the art prefetching method. These results indicate that our approach significantly and is a practical method that can be implemented in commercial ORDMSs.