• KIPS Transactions on Software and Data Engineering
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• v.9 no.12
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• pp.411-418
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• 2020

In the field of information security, IDS(Intrusion Detection System) is normally classified in two different categories: signature-based IDS and anomaly-based IDS. Many studies in anomaly-based IDS have been conducted that analyze network traffic data generated in cyberspace by machine learning algorithms. In this paper, we studied pre-processing methods to overcome performance degradation problems cashed by rare classes. We experimented classification performance of a Machine Learning algorithm by reconstructing data set based on rare classes and semi rare classes. After reconstructing data into three different sets, wrapper and filter feature selection methods are applied continuously. Each data set is regularized by a quantile scaler. Depp neural network model is used for learning and validation. The evaluation results are compared by true positive values and false negative values. We acquired improved classification performances on all of three data sets.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.3
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• pp.551-561
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• 2018

As the computational technology using quantum computing has been developed, several threats on cryptographic systems are recently increasing. Therefore, many researches on post-quantum cryptosystems which can withstand the analysis attacks using quantum computers are actively underway. Nevertheless, the lattice-based NTRU system, one of the post-quantum cryptosystems, is pointed out that it may be vulnerable to the fault injection attack which uses the weakness of implementation of NTRU. In this paper, we investigate the fault injection attacks and their previous countermeasures on the NTRU signature system and propose a secure and efficient countermeasure to defeat it. As a simulation result, the proposed countermeasure has high fault detection ratio and low implementation costs.

• Journal of Communications and Networks
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• v.13 no.2
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• pp.187-200
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• 2011

We present the design and implementation of a novel anti-malware system called SplitScreen. SplitScreen performs an additional screening step prior to the signature matching phase found in existing approaches. The screening step filters out most non-infected files (90%) and also identifiesmalware signatures that are not of interest (99%). The screening step significantly improves end-to-end performance because safe files are quickly identified and are not processed further, and malware files can subsequently be scanned using only the signatures that are necessary. Our approach naturally leads to a network-based anti-malware solution in which clients only receive signatures they needed, not every malware signature ever created as with current approaches. We have implemented SplitScreen as an extension to ClamAV, the most popular open source anti-malware software. For the current number of signatures, our implementation is $2{\times}$ faster and requires $2{\times}$ less memory than the original ClamAV. These gaps widen as the number of signatures grows.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.820-824
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• 2003

One of the most important research areas on remote sensing is spectral unmixing of hyper-spectral data. For spectral unmixing of hyper spectral data, accurate land cover information is necessary. But obtaining accurate land cover information is difficult process. Obtaining land cover information from high-resolution data may be a useful solution. In this study spectral signature of endmembers on ASTER acquired in October was calculated from land cover information on IKONOS acquired in September. Then the spectral signature of endmembers applied to ASTER images acquired on January and March. Then the result of spectral unmxing of them evauateted. The spectral signatures of endmembers could be applied to different seasonal images. When it applied to an ASTER image which have similar zenith angle to the image of the spectral signatures of endmembers, spectral unmixing result was reliable. Although test data has different zenith angle from the image of spectral signatures of endmembers, the spectral unmixing results of urban and vegetation were reliable.

• Journal of the Korea Society of Computer and Information
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• v.19 no.1
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• pp.85-94
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• 2014

This paper studied the detection technique using file DNA-based behavior pattern analysis in order to minimize damage to user system by malicious programs before signature or security patch is released. The file DNA-based detection technique was applied to defend against zero day attack and to minimize false detection, by remedying weaknesses of the conventional network-based packet detection technique and process-based detection technique. For the file DNA-based detection technique, abnormal behaviors of malware were splitted into network-related behaviors and process-related behaviors. This technique was employed to check and block crucial behaviors of process and network behaviors operating in user system, according to the fixed conditions, to analyze the similarity of behavior patterns of malware, based on the file DNA which process behaviors and network behaviors are mixed, and to deal with it rapidly through hazard warning and cut-off.

• Convergence Security Journal
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• v.22 no.5
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• pp.145-154
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• 2022

Recently, the widespread proliferation and high sophistication of botnets are having serious consequences not only for enterprises and users, but also for cyber warfare between countries. Therefore, research to detect botnets is steadily progressing. However, the DGA-based botnet has a high detection rate with the existing signature and statistics-based technology, but also has a high limit in the false positive rate. Therefore, in this paper, we propose a detection model using text-based n-gram to detect DGA-based botnets. Through the proposed model, the detection rate, which is the limit of the existing detection technology, can be increased and the false positive rate can also be minimized. Through experiments on large-scale domain datasets and normal domains used in various DGA botnets, it was confirmed that the performance was superior to that of the existing model. It was confirmed that the false positive rate of the proposed model is less than 2 to 4%, and the overall detection accuracy and F1 score are both 97.5%. As such, it is expected that the detection and response capabilities of DGA-based botnets will be improved through the model proposed in this paper.

• Journal of the Society of Naval Architects of Korea
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• v.60 no.1
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• pp.57-64
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• 2023

Recently, the increased use of anti-ship guided missiles, a weapon system that detects and attacks targets in naval engagement, has come to pose a major threat to the survivability of ships. In order to improve the survivability of ships in response to such anti-ship guided missiles, many studies of means to counteract them have been conducted in militarily advanced countries. The integrated survivability of a ship can be largely divided into susceptibility, vulnerability, and recoverability, and is expressed as the conditional probability, if the ship is hit, of damage and recovery. However, as research on susceptibility is a major military secret of each country, access to it is very limited and there are few publicly available data. Therefore, in this study, a possibility of estimating the susceptibility of ships using an anti-air defense system corresponding to anti-ship guided missiles was reviewed. To this, scenarios during engagement, weapon systems mounted to counter threats, and maximum detection/battle range according to the operational situation of the defense weapon system were defined. In addition, the effectiveness of the anti-air defense system and susceptibility was calculated based on the performance of the weapon system, the crew's ability to operate the weapon system, and the detection probability of the detection/defense system. To evaluate the susceptibility estimation feasibility, the sensitivity of the detailed variables was reviewed, and the usefulness of the established process was confirmed through sensitivity analysis.

• International Commerce and Information Review
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• v.9 no.3
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• pp.305-320
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• 2007

With expanded use of B2B(between enterprises), B2G(between enterprises and government) and EDI(Electronic Data Interchange), and increased amount of available network information and information protection threat, as it was judged that security can not be perfectly assured only with security technology such as electronic signature/authorization and access control, Bayesian networks have been developed for protection of information. Therefore, this study speculates Bayesian networks system, centering on ERP(Enterprise Resource Planning). The Bayesian networks system is one of the methods to resolve uncertainty in electronic data interchange and is applied to overcome uncertainty of abnormal invasion detection in ERP. Bayesian networks are applied to construct profiling for system call and network data, and simulate against abnormal invasion detection. The host-based abnormal invasion detection system in electronic trade analyses system call, applies Bayesian probability values, and constructs normal behavior profile to detect abnormal behaviors. This study assumes before and after of delivery behavior of the electronic document through Bayesian probability value and expresses before and after of the delivery behavior or events based on Bayesian networks. Therefore, profiling process using Bayesian networks can be applied for abnormal invasion detection based on host and network. In respect to transmission and reception of electronic documents, we need further studies on standards that classify abnormal invasion of various patterns in ERP and evaluate them by Bayesian probability values, and on classification of B2B invasion pattern genealogy to effectively detect deformed abnormal invasion patterns.

• Journal of KIISE
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• v.45 no.1
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• pp.1-8
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• 2018

Recently, distribution of malicious codes using the Internet has been one of the most serious cyber threats. Technology of malicious code distribution with detection bypass techniques has been also developing and the research has focused on how to detect and analyze them. However, obfuscated malicious JavaScript is almost impossible to detect, because the existing malicious code distributed web page detection system is based on signature and another limitation is that it requires constant updates of the detection patterns. We propose to overcome these limitations by means of an intelligent malicious code distributed web page detection system using a real browser that can analyze and detect intelligent malicious code distributed web sites effectively.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.7
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• pp.613-621
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• 2019

Recently, with the development of the Internet of Things (IoT) and cloud computing technologies, security threats have increased as malicious codes infect IoT devices, and new malware spreads ransomware to cloud servers. In this study, we propose a threat-detection technique that checks obfuscated script patterns to compensate for the shortcomings of conventional signature-based and behavior-based detection methods. Proposed is a malicious code-detection technique that is based on malicious script-pattern analysis that can detect zero-day attacks while maintaining the existing detection rate by registering and checking derived distribution patterns after analyzing the types of malicious scripts distributed through websites. To verify the performance of the proposed technique, a prototype system was developed to collect a total of 390 malicious websites and experiment with 10 major malicious script-distribution patterns derived from analysis. The technique showed an average detection rate of about 86% of all items, while maintaining the existing detection speed based on the detection rule and also detecting zero-day attacks.