• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.5
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• pp.1167-1177
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• 2019

In this paper, the improved security operation model based on the vulnerability database is studied. The proposed model consists of information protection equipment, vulnerability database, and a dashboard that visualizes and provides the results of interworking with detected logs. The evaluation of the model is analyzed by setting up a simulated attack scenario in a virtual infrastructure. In contrast to the traditional method, it is possible to respond quickly to threats of attacks specific to the security vulnerabilities that the asset has, and to find redundancy between detection rules with a secure agent, thereby creating an optimal detection rule.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.4
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• pp.821-834
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• 2019

As cyber security threats increase, there is a growing demand for highly reliable systems. Common Criteria, an international standard for evaluating information security products, requires formal specification and verification of the system to ensure a high level of security, and more and more cases are being observed. In this paper, we propose highly reliable drone systems that ensure high level security level and trust. Based on the results, we use formal methods especially Z/EVES to improve the system model in terms of scheduling in the system kernel.

• Journal of Digital Convergence
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• v.17 no.9
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• pp.161-171
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• 2019

Government transactions over wireless electronic devices are not safe and hence the messages are prone to attack. Thereby, devices supporting wireless Internet must assure the same level of security and privacy as the wired network. National public key infrastructure (NPKI) for electronic government used in the wired environment is not suitable for wireless environment for mobile government (mGovernment) because of the limitations of computing power, memory capacity and restricted battery power. This requires the development of a new NPKI for mGovernment, denoted as mNPKI, to developing countries, which provides the same security level as the wired NPKI. For the wireless environment requirements, mNPKI is based on short lived certificates. Analysis shows that mNPKI is well suited to wireless Internet and provides the same security requirement from the wired NPKI.

• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.3
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• pp.281-301
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• 2023

The shipboard combat system is a key system for naval combat that supports a command and control process cycle consisting of Detect - Control - Engage in real time to ensure ship viability and conduct combat missions. Modern combat systems were developed on the basis of Open Architecture(OA) to maximize acceptance of latest technology and interoperability between systems, and actively introduced the COTS(Commercial-of-the-shelf). However, as a result of that, vulnerabilities inherent in COTS SW and HW also occurred in the combat system. The importance of combat system cybersecurity is being emphasized but cybersecurity research reflecting the characteristics of the combat system is still lacking in Korea. Therefore, in this paper, we systematically identify combat system threats by applying Data Flow Diagram, Microsoft STRIDE threat modelling methodology. The threats were analyzed using the Attack Tree & Misuse case. Finally we derived the applicable security requirements which can be used at stages of planning and designing combat system and verified security requirements through NIST 800-53 security control items.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1067-1076
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• 2023

Recently, as variant malware has increased, the scale of cyber hacking incidents is expanding. To respond to intelligent cyberhacking attack, machine learning-based research is actively underway to effectively classify malware families. However, existing classification models have problems where performance deteriorates when the dataset is obfuscated or sparse. In this paper, we propose a hybrid dataset that combines features extracted from ASM files and BYTES files, and evaluate classification performance using FNN. As a result of the experiment, the proposed method showed performance improvement of about 4% compared to a single dataset, and in particular, performance improvement of about 30% for rare families.

• Journal of Internet Computing and Services
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• v.18 no.1
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• pp.11-20
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• 2017

Recently, developments in the various fields of cloud computing technology has been utilized. Whereas the demand for cloud computing services is increasing, security threats are also increasing in the cloud computing environments. Especially, in case when the hosts interconnected in the cloud environments are infected and propagated through the attacks by malware. It can have an effect on the resource of other hosts and other security threats such as personal information can be spreaded and data deletion. Therefore, the study of malware analysis to respond these security threats has been proceeded actively. This paper proposes a type of attack clustering method of Zeus botnet using the k-means clustering algorithm for malware analysis that occurs in the cloud environments. By clustering the malicious activity by a type of the Zeus botnet occurred in the cloud environments. it is possible to determine whether it is a malware or not. In the future, it sets a goal of responding to an attack of the new type of Zeus botnet that may occur in the cloud environments.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.2
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• pp.385-395
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• 2018

With the development of Internet, cyber attack has become a major threat. To detect cyber attacks, intrusion detection system(IDS) has been widely deployed. But IDS has a critical weakness which is that it generates a large number of false alarms. One of the promising techniques that reduce the false alarms in real time is machine learning. However, there are problems that must be solved to use machine learning. So, many machine learning approaches have been applied to this field. But so far, researchers have not focused on features. Despite the features of IDS alerts are important for performance of model, the approach to feature is ignored. In this paper, we propose new feature set which can improve the performance of model and can be extracted from a single alarm. New features are motivated from security analyst's know-how. We trained and tested the proposed model applied new feature set with real IDS alerts. Experimental results indicate the proposed model can achieve better accuracy and false positive rate than SVM model with ordinary features.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.4
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• pp.847-857
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• 2018

As the number of malicious code increases steeply, cyber attack victims targeting corporations, public institutions, financial institutions, hospitals are also increasing. Accordingly, academia and security industry are conducting various researches on malicious code detection. In recent years, there have been a lot of researches using machine learning techniques including deep learning. In the case of research using Convolutional Neural Network, ResNet, etc. for classification of malicious code, it can be confirmed that the performance improvement is higher than the existing classification method. However, one of the characteristics of the target attack is that it is custom malicious code that makes it operate only for a specific company, so it is not a form spreading widely to a large number of users. Since there are not many malicious codes of this kind, it is difficult to apply the previously studied machine learning or deep learning techniques. In this paper, we propose a method to classify malicious codes when the amount of samples is insufficient such as targeting type malicious code. As a result of the study, we confirmed that the accuracy of 97% can be achieved even with a small amount of data by applying the Memory Augmented Neural Networks model.

• Smart Media Journal
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• v.13 no.6
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• pp.9-15
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• 2024

In recent trends, there has been an increase in 'Qshing' attacks, a hybrid form of phishing that exploits fake QR (Quick Response) codes impersonating government agencies to steal personal and financial information. Particularly, this attack method is characterized by its stealthiness, as victims can be redirected to phishing pages or led to download malicious software simply by scanning a QR code, making it difficult for them to realize they have been targeted. In this paper, we have developed a classification technique utilizing machine learning algorithms to identify the maliciousness of URLs embedded in QR codes, and we have explored ways to integrate this with existing QR code readers. To this end, we constructed a dataset from 128,587 malicious URLs and 428,102 benign URLs, extracting 35 different features such as protocol and parameters, and used AutoML to identify the optimal algorithm and hyperparameters, achieving an accuracy of approximately 87.37%. Following this, we designed the integration of the trained classification model with existing QR code readers to implement a service capable of countering Qshing attacks. In conclusion, our findings confirm that deriving an optimized algorithm for classifying malicious URLs in QR codes and integrating it with existing QR code readers presents a viable solution to combat Qshing attacks.

• The Journal of Society for e-Business Studies
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• v.20 no.4
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• pp.127-150
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• 2015

Since 9/11 attack, internet has become a major space for terrorist activities and also emerged as the most important spot of lone wolf terrorists for acquiring tools and radicalization. The accident of student Kim's defection to IS (Islamic state) in January 2015 told us that Korea is not any more "terrorism clearance area" and leaded us to look closely into the possibility of lone wolf terrorist. In this paper, I developed a "lone wolf cyber evolution model" using various materials collected by preceding papers and interviewing investigators and terrorism experts in Korea. I analyze Kim's radicalization process using this model. And I picked and closely looked over some facilitating factors of lone wolf such as multi-cultural socialization, increase of international migrants, expansion alienation hierarchy and ideological conflicts deepening and predicted the possibility of lone wolf. Finally, this paper presents some effective policy measurements against lone wolf terrorism in Korea.