• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3926-3945
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• 2018

In Jøsang's trust model, trust evaluation is obtained through operators, but there are problems with the mutuality and asymmetry of trust and the impact of event weight on trust evaluation. Trust evaluation is updated dynamically and continuously with time and the process of interactions, but it has not been reflected in Jøsang's model. Therefore, final trust evaluation is not accurate, and malicious fraud cannot be prevented effectively. This causes the success rate of interaction to be low. To solve these problems, a new dynamic trust model is proposed based on extended subjective logic (DTM-ESL). In DTM-ESL, the event weight and the mutuality of trust are fully considered, the original one-way trust relationship is extended to a two-way trust relationship, discounting and consensus operators are improved, and trust renewal is designed based on event weight. The viability and effectiveness of this new model are verified by simulation experiments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2101-2123
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• 2023

Recent studies have shown that the neural network-based binary code similarity detection technology performs well in vulnerability mining, plagiarism detection, and malicious code analysis. However, existing cross-architecture methods still suffer from insufficient feature characterization and low discrimination accuracy. To address these issues, this paper proposes a cross-architecture binary function similarity detection method based on composite feature model (SDCFM). Firstly, the binary function is converted into vector representation according to the proposed composite feature model, which is composed of instruction statistical features, control flow graph structural features, and application program interface calling behavioral features. Then, the composite features are embedded by the proposed hierarchical embedding network based on a graph neural network. In which, the block-level features and the function-level features are processed separately and finally fused into the embedding. In addition, to make the trained model more accurate and stable, our method utilizes the embeddings of predecessor nodes to modify the node embedding in the iterative updating process of the graph neural network. To assess the effectiveness of composite feature model, we contrast SDCFM with the state of art method on benchmark datasets. The experimental results show that SDCFM has good performance both on the area under the curve in the binary function similarity detection task and the vulnerable candidate function ranking in vulnerability search task.

• The Transactions of the Korea Information Processing Society
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• v.7 no.4
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• pp.1184-1192
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• 2000

Multicast has communication mechanism that is able to transfer voice, video for only the specific user group. As compared to unicast, multicast is more susceptive to attack such as masquerading, malicious replay, denial of service, repudiation and traffic observation, because of the multicast has much more communication links than unicast communication. Multicast-specific security threats can affect not only a group's receivers, but a potentially large proportion of the internet. In this paper, we proposed the multicast security model that is able to secure multi-group communication in CBT(Core Based Tree), which is multicast routing. And designed the multicast key distribution protocol that can offer authentication, user privacy using core (be does as Authentication Server) in the proposed model.

• Proceedings of the Korea Information Processing Society Conference
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• 2005.05a
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• pp.1461-1464
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• 2005

In contrast with conventional networks, mobile ad hoc networks usually do not provide online access to trusted authorities or to centralized servers, and they exhibit frequent partitioning due to link and node failures and node mobility. For these reasons, traditional security solutions that require online trusted authorities or certificate repositories, but those are not well-suited for securing ad hoc networks. Moreover, a fundamental issue of securing mobile ad hoc networks is to ensure mobile nodes can authenticate each other. Because of its strength and efficiency, public key and digital signature is an ideal mechanism to construct the authentication service. Although this is already mature in the internet application, providing public key based authentication is still very challenging in mobile ad hoc networks. In this paper I propose a secure public key authentication service based on clustering model and trust model to protect nodes from getting false public keys of the others efficiently when there are malicious nodes in the network.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.3B
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• pp.557-564
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• 2000

This paper presents a watermarking algorithm for embedding visually recognizable pattern (Mark, Logo, Symbol, stamping or signature) into the image. first, the color image(RGB model)is transformed in YCbCr model and then the Y component is transformed into 3-level wavelet transform. Next, the values are assembled with pattern watermark. PN(pseudo noise) code at spread spectrum communication method and mutilevel watermark weights. This values are inserted into discrete wavelet domain. In our scheme, new calculating method is designed to calculate wavelet transform with integer value in considering the quantization error. and we used the color conversion with fixed-point arithmetic to be easy to make the hardware hereafter. Also, we made the new solution using mutilevel threshold to robust to common signal distortions and malicious attack, and to enhance quality of image in considering the human visual system. the experimental results showed that the proposed watermarking algorithm was superior to other similar water marking algorithm. We showed what it was robust to common signal processing and geometric transform such as brightness. contrast, filtering. scaling. JPEG lossy compression and geometric deformation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.947-950
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• 2014

The recent APT attacks including cyber terror are caused by a high level of malicious codes and hacking techniques. This implies that essentially, advanced security management is required, from the perspective of 5A. The changes of IT environment are represented by Mobile, Cloud and BYOD. In this situation, the security model needs to be changed, too into the Airport model which emphasizes prevention, and connection, security and integration of functions from the existing Castle model. This study suggested an application method of the risk-based Airport model to the cyber security environment.

• Journal of Internet Computing and Services
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• v.18 no.6
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• pp.35-46
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• 2017

Recently, attackers using malicious code in cyber security have been increased by attaching malicious code to a mail and inducing the user to execute it. Especially, it is dangerous because it is easy to execute by attaching a document type file. The author analysis is a research area that is being studied in NLP (Neutral Language Process) and text mining, and it studies methods of analyzing authors by analyzing text sentences, texts, and documents in a specific language. In case of attack mail, it is created by the attacker. Therefore, by analyzing the contents of the mail and the attached document file and identifying the corresponding author, it is possible to discover more distinctive features from the normal mail and improve the detection accuracy. In this pager, we proposed IADA2(Intelligent Attack mail Detection based on Authorship Analysis) model for attack mail detection. The feature vector that can classify and detect attack mail from the features used in the existing machine learning based spam detection model and the features used in the author analysis of the document and the IADA2 detection model. We have improved the detection models of attack mails by simply detecting term features and extracted features that reflect the sequence characteristics of words by applying n-grams. Result of experiment show that the proposed method improves performance according to feature combinations, feature selection techniques, and appropriate models.

• The Journal of the Korea Contents Association
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• v.21 no.2
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• pp.121-129
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• 2021

In this study, we try to minimize the tariff risk by constructing a hazardous cargo screening model by applying Association Rule Mining, one of the data mining techniques. For this, the risk level between supply chains is calculated using the Apriori Algorithm, which is an association analysis algorithm, using the big data of the import declaration form of the Korea Customs Service(KCS). We perform data preprocessing and association rule mining to generate a model to be used in screening the supply chain. In the preprocessing process, we extract the attributes required for rule generation from the import declaration data after the error removing process. Then, we generate the rules by using the extracted attributes as inputs to the Apriori algorithm. The generated association rule model is loaded in the KCS screening system. When the import declaration which should be checked is received, the screening system refers to the model and returns the confidence value based on the supply chain information on the import declaration data. The result will be used to determine whether to check the import case. The 5-fold cross-validation of 16.6% precision and 33.8% recall showed that import declaration data for 2 years and 6 months were divided into learning data and test data. This is a result that is about 3.4 times higher in precision and 1.5 times higher in recall than frequency-based methods. This confirms that the proposed method is an effective way to reduce tariff risks.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.25-36
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• 2023

Today, as AI (Artificial Intelligence) technology is introduced in various fields, including security, the development of technology is accelerating. However, with the development of AI technology, attack techniques that cleverly bypass malicious behavior detection are also developing. In the classification process of AI models, an Adversarial attack has emerged that induces misclassification and a decrease in reliability through fine adjustment of input values. The attacks that will appear in the future are not new attacks created by an attacker but rather a method of avoiding the detection system by slightly modifying existing attacks, such as Adversarial attacks. Developing a robust model that can respond to these malware variants is necessary. In this paper, we propose two methods of generating Adversarial attacks as efficient Adversarial attack generation techniques for improving Robustness in AI models. The proposed technique is the XAI-based attack technique using the XAI technique and the Reference based attack through the model's decision boundary search. After that, a classification model was constructed through a malicious code dataset to compare performance with the PGD attack, one of the existing Adversarial attacks. In terms of generation speed, XAI-based attack, and reference-based attack take 0.35 seconds and 0.47 seconds, respectively, compared to the existing PGD attack, which takes 20 minutes, showing a very high speed, especially in the case of reference-based attack, 97.7%, which is higher than the existing PGD attack's generation rate of 75.5%. Therefore, the proposed technique enables more efficient Adversarial attacks and is expected to contribute to research to build a robust AI model in the future.

• Convergence Security Journal
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• v.6 no.3
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• pp.1-12
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• 2006

Malicious codes have been widely documented and detected in information security breach occurrences of Microsoft Windows platform. Legacy information security systems are particularly vulnerable to breaches, due to Window kernel-based malicious codes, that penetrate existing protection and remain undetected. To date there has not been enough quality study into and information sharing about Windows kernel and inner code mechanisms, and this is the core reason for the success of these codes into entering systems and remaining undetected. This paper focus on classification and formalization of type target and mechanism of various Windows kernel-based attacks, and will present suggestions for effective response methodologies in the categories of, "Kernel memory protection", "Process & driver protection" and "File system & registry protection". An effective Windows kernel protection system will be presented through the collection and analysis of Windows kernel and inside mechanisms, and through suggestions for the implementation methodologies of unreleased and new Windows kernel protection skill. Results presented in this paper will explain that the suggested system be highly effective and has more accurate for intrusion detection ratios, then the current legacy security systems (i.e., virus vaccines and Windows IPS, etc) intrusion detection ratios. So, It is expected that the suggested system provides a good solution to prevent IT infrastructure from complicated and intelligent Windows kernel attacks.