• Convergence Security Journal
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• v.20 no.5
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• pp.19-26
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• 2020

The purpose of this paper is to ccompare and analyze North Korean cyber attacks and our responses by government, from the Roh Moo-hyun administration to the Moon Jae-in administration. The current conflict of interests on the Korean peninsula, such as the United States, China, and Russia, is leading to a conflict for the leadership of a new world order in cyberspace. Cyber attacks are accelerating and threats are rising. Cyber threats exhibit several characteristics. Above all, it is difficult to identify or track the subject of the threat. Also, with the development of information and communication technology, attack technology has become more intelligent, and it is not easy to prepare a means to respond. Therefore, it is necessary to improve continuous and preemptive response capacity for national cybersecurity, and to establish governance among various actors, such as international cooperation between countries or private experts.

• 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 Institute of Information Security & Cryptology
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• v.26 no.5
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• pp.1313-1322
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• 2016

Recently, many virus and hacking attacks on public organizations and financial institutions by internet are becoming increasingly intelligent and sophisticated. The Advanced Persistent Threat has been considered as an important cyber risk. This attack is basically accomplished by spreading malicious codes through complex networks. To detect and extract PE files in smart manufacturing industry networks, an efficient processing method which is performed before analysis procedure on malicious codes is proposed. We implement a preprocessor of open intrusion detection system Snort for fast extraction of PE files and install on a hardware sensor equipment. As a result of practical experiment, we verify that the network sensor can extract the PE files which are often suspected as a malware.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.432-435
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• 2022

최근 IT 산업의 지속적인 발전으로 사용자들을 위협하는 악성코드, 피싱, 랜섬웨어와 같은 사이버 공격 또한 계속해서 발전하고 더 지능화되고 있으며 변종 악성코드도 기하급수적으로 늘어나고 있다. 지금까지의 시그니처 패턴 기반의 탐지법으로는 이러한 방대한 양의 알려지지 않은 악성코드를 탐지할 수 없다. 따라서 CNN(Convolutional Neural Network)을 활용하여 악성코드를 탐지하는 기법들이 제안되고 있다. 이에 본 논문에서는 CNN 모델 중 낮은 인식 오류율을 지닌 모델을 선정하여 정확도(Accuracy)와 F1-score 평가 지표를 통해 비교하고자 한다. 두 가지의 악성코드 이미지화 방법을 사용하였으며, 2015 년 이후 ILSVRC 에서 우승을 차지한 모델들과, 추가로 2019 년에 발표된 EfficientNet 을 사용하여 악성코드 이미지를 분류하였다. 그 결과 2 바이트를 한 쌍의 좌표로 변환하여 생성한 256 * 256 크기의 악성코드 이미지를 ResNet-152 모델을 이용해 분류하는 것이 우수한 성능을 보임을 실험적으로 확인하였다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.3
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• pp.769-776
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• 2016

Recently, cyber attacks are continuously threatening the cyberspace of the state across the border. Such cyber attacks show a surface which is intelligent and sophisticated level that can paralyze key infrastructure in the country. It can be seen well in cases, such as hacking threat of nuclear power plant, 3.20 cyber terrorism. Especially in public institutions of the country in which there is important information of the country, advanced prevention is important because the large-scale damage is expected to such cyber attacks. Technical support is also important, but by improving the cyber security awareness and security expert knowledge through the cyber security education to the country's public institutions workers is important to raise the security level. This paper suggest education courses for the rise of the best security effect through a short-term course for the country's public institutions workers.

• Journal of Digital Contents Society
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• v.11 no.1
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• pp.79-89
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• 2010

The government and companies build a DDoS correspondence system hastily to protect assets from cyber threats. It has become more and more intelligent and advanced such as DDoS attack. However, when outbreaks of the social incidents such as 7.7 DDoS attack(2009.7.7) or cases of the direct damage occurred, information security systems(ISS) only become the issue in the short term. As usual, sustained investment about ISS is a negative recognition. Since the characteristic of ISS is hard to recognize the effectiveness of them before incidents occurs. Also, results of incidents occurred classify attack and detection. Detailed and objective measurement criterion to measure effectiveness and efficiency of ISS is not existed. Recently, it is progress that evaluation and certification about for the information security management system(ISMS). Since these works propose only a general guideline, it is difficult to utilize as a result of ISMS improvement for organization. Therefore, this paper proposes a framework to develop main criteria by a correspondence strategy and process. It is able to detailed and objective measurements.

• Journal of Internet Computing and Services
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• v.22 no.1
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• pp.13-22
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• 2021

Recently network based attack technologies are rapidly advanced and intelligent, the limitations of existing signature-based intrusion detection systems are becoming clear. The reason is that signature-based detection methods lack generalization capabilities for new attacks such as APT attacks. To solve these problems, research on machine learning-based intrusion detection systems is being actively conducted. However, in the actual network environment, attack samples are collected very little compared to normal samples, resulting in class imbalance problems. When a supervised learning-based anomaly detection model is trained with such data, the result is biased to the normal sample. In this paper, we propose to overcome this imbalance problem through One-Class Anomaly Detection using an auto encoder. The experiment was conducted through the NSL-KDD data set and compares the performance with the supervised learning models for the performance evaluation of the proposed method.

• Journal of Software Assessment and Valuation
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• v.15 no.2
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• pp.121-128
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• 2019

Computing systems have various vulnerabilities to cyber attacks. In particular, various cyber attacks that are intelligent in the information society have caused serious social problems and economic losses. Traditional security systems are based on misuse-based technology, which requires the continuous updating of new attack patterns and the real-time analysis of vast amounts of data generated by numerous security devices in order to accurately detect. However, traditional security systems are unable to respond through detection and analysis in real time, which can delay the recognition of intrusions and cause a lot of damage. Therefore, there is a need for a new security system that can quickly detect, analyze, and predict the ever-increasing cyber security threats based on machine learning and big data analysis models. In this paper, we present a IDS model that combines machine learning and big data technology.

• Convergence Security Journal
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• v.23 no.4
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• pp.33-41
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• 2023

In recent years, advanced persistent threat (APT) attack organizations have exploited various vulnerabilities and attack techniques to target companies and institutions with national core technologies, distributing ransomware and demanding payment, stealing nationally important industrial secrets and distributing them on the black market (dark web), selling them to third countries, or using them to close the technology gap, requiring national-level security preparations. In this paper, we analyze the attack methods of attack organizations such as Kimsuky and Lazarus that caused industrial secrets leakage damage through APT attacks in Korea using the MITRE ATT&CK framework, and derive 26 cybersecurity-related administrative, physical, and technical security requirements that a company's security system should be equipped with. We also proposed a security framework and system configuration plan to utilize the security requirements in actual field. The security requirements presented in this paper provide practical methods and frameworks for security system developers and operators to utilize in security work to prevent leakage of corporate industrial secrets. In the future, it is necessary to analyze the advanced and intelligent attacks of various APT attack groups based on this paper and further research on related security measures.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.8
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• pp.355-364
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• 2023

As advanced cyber threats continue to increase in recent years, it is difficult to detect new types of cyber attacks with existing pattern or signature-based intrusion detection method. Therefore, research on anomaly detection methods using data learning-based artificial intelligence technology is increasing. In addition, supervised learning-based anomaly detection methods are difficult to use in real environments because they require sufficient labeled data for learning. Research on an unsupervised learning-based method that learns from normal data and detects an anomaly by finding a pattern in the data itself has been actively conducted. Therefore, this study aims to extract a latent vector that preserves useful sequence information from sequence log data and develop an anomaly detection learning model using the extracted latent vector. Word2Vec was used to create a dense vector representation corresponding to the characteristics of each sequence, and an unsupervised autoencoder was developed to extract latent vectors from sequence data expressed as dense vectors. The developed autoencoder model is a recurrent neural network GRU (Gated Recurrent Unit) based denoising autoencoder suitable for sequence data, a one-dimensional convolutional neural network-based autoencoder to solve the limited short-term memory problem that GRU can have, and an autoencoder combining GRU and one-dimensional convolution was used. The data used in the experiment is time-series-based NGIDS (Next Generation IDS Dataset) data, and as a result of the experiment, an autoencoder that combines GRU and one-dimensional convolution is better than a model using a GRU-based autoencoder or a one-dimensional convolution-based autoencoder. It was efficient in terms of learning time for extracting useful latent patterns from training data, and showed stable performance with smaller fluctuations in anomaly detection performance.