• Journal of Internet Computing and Services
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• v.7 no.5
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• pp.109-121
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• 2006

In this paper, we suggest lattice based role graph security management model which changes security level in mandatory access control model as well as constraint and role hierarchy systematically in role base access control model. In this model, we solved privilege abuse of senior role that is role graph model's problem, and when produce conflict between privileges, we can keep integrity of information by reseting grade of subject through constraint. Also, we offer strong security function by doing to be controlled by subject's security level as well as privilege inheritance by role hierarchy, Finally, we present the role graph algorithms with logic to disallow roles that contain conflicting privileges.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.5
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• pp.450-457
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• 2004

In this research, the automatic synthesis of knowledge models is proposed. which are the basis of the methods using qualitative models adapted widely in fault diagnosis and hazard evaluation of chemical processes. To provide an easy and fast way to construct accurate causal model of the target process, the Role-Behavior modeling method is developed to represent the knowledge of modularized process units. In this modeling method, Fault-Behavior model and Structure-Role model present the relationship of the internal behaviors and faults in the process units and the relationship between process units respectively. Through the multiple modeling techniques, the knowledge is separated into what is independent of process and dependent on process to provide the extensibility and portability in model building, and possibility in the automatic synthesis. By taking advantage of the Role-Behavior Model, an algorithm is proposed to synthesize the plant-wide causal model, Fault-Causality Graph (FCG) from specific Fault-Behavior models of the each unit process, which are derived from generic Fault-Behavior models and Structure-Role model. To validate the proposed modeling method and algorithm, a system for building FCG model is developed on G2, an expert system development tool. Case study such as CSTR with recycle using the developed system showed that the proposed method and algorithm were remarkably effective in synthesizing the causal knowledge models for diagnosis of chemical processes.

• Journal of the Korea Society of Computer and Information
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• v.14 no.6
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• pp.91-98
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• 2009

In this paper, this model ensures confidentiality and integrity of mandatory access cotrol policy which based on fuzzy function with importance of information. And it solves authorization abuse problem through role graph creation algorithm and flowing policy that security grade is applied. Because this model composes role hierarchy which bind similar role concept to apply to commercial environment, it has expansile advantage by large scale security system as well as is easy that add new role.

• Journal of Korea Multimedia Society
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• v.7 no.11
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• pp.1620-1629
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• 2004

The objectives of access control are to protect computing and communication resources from illegal use, alteration, disclosure and destruction by unauthorized users. Although Biba security model is well suited for protecting the integrity of information, it is considered too restrictive to be an access control model for commercial environments. And, Role-Based Access Control(RBAC) model, a flexible and policy-neutral security model that is being widely accepted in commercial areas, has a possibility for compromising integrity of information. In this paper, We present the role graph model which enhanced flexibility and integrity to management of many access permission. Also, In order to represent those rule and constraints clearly, formal descriptions of role assignment rule and constraints in Z language are also given.

• The Journal of Society for e-Business Studies
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• v.26 no.3
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• pp.67-80
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• 2021

The node embedding technique for learning graph representation plays an important role in obtaining good quality results in graph mining. Until now, representative node embedding techniques have been studied for homogeneous graphs, and thus it is difficult to learn knowledge graphs with unique meanings for each edge. To resolve this problem, the conventional Triple2Vec technique builds an embedding model by learning a triple graph having a node pair and an edge of the knowledge graph as one node. However, the Triple2 Vec embedding model has limitations in improving performance because it calculates the relationship between triple nodes as a simple measure. Therefore, this paper proposes a feature extraction technique based on a graph convolutional neural network to improve the Triple2Vec embedding model. The proposed method extracts the neighborliness vector of the triple graph and learns the relationship between neighboring nodes for each node in the triple graph. We proves that the embedding model applying the proposed method is superior to the existing Triple2Vec model through category classification experiments using DBLP, DBpedia, and IMDB datasets.

• Journal of KIISE
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• v.44 no.10
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• pp.1034-1044
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• 2017

Although traversal strategies are important for the performance of model checking, many studies have ignored the impact of traversal strategies in model checking with a block-encoded abstract reachability graph. Studies have considered traversal strategies only for an abstract reachability graph without block-encoding. Block encoding plays a crucial role in the model checking performance. This paper therefore describes Dual-traversal strategy, a simple and novel technique to control traversal strategies in a block-encoded abstract reachability graph. This method uses two traversal strategies for a model checking, one for effective block-encoding, and the other for traversal in an encoded abstract reachability graph. Dual-traversal strategy is very simple and can be implemented without overhead compared to the existing single-traversal strategy. We implemented the Dual-traversal strategy in an open source model checking tool and compare the performances of different traversal strategies. The results show that the model checking performance varies from the traversal strategies for the encoded abstract reachability graph.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.984-987
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• 2018

In an Internet of Thing (IoT) environment, entities with different attributes and capacities are going to be connected in a highly connected fashion. Specifically, not only the mechanical and electronic devices but also other entities such as people, locations and applications are connected to each other. Understanding and managing these connections play an important role for businesses, which identify opportunities for new IoT services. Traditional approach for storing and querying IoT data is used of a relational database management system (RDMS) such as MySQL or MSSQL. However, using RDMS is not flexible and sufficient for handling heterogeneous IoT data because these data have deeply complex relationships which require nested queries and complex joins on multiple tables. In this paper, we propose a graph model for constructing a graph database of heterogeneous IoT data. Graph databases are purposely-built to store highly connected data with nodes representing entities and edges representing the relationships between these entities. Our model fuses social graph, spatial graph, and things graph, and incorporates the relationships among them. We then present a case study which applies our model for representing data from a Smart Campus using Neo4J platform. Through the results of querying to answer real questions in Smart Campus management, we show the viability of our model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5731-5754
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• 2019

In this paper, we redesign, implement, and evaluate ShareSafe (Based on SecGraph), an open-source secure graph data sharing/publishing platform. Within ShareSafe, we propose De-anonymization Quantification Module and Recommendation Module. Besides, we model the attackers' background knowledge and evaluate the relation between graph data privacy and the structure of the graph. To the best of our knowledge, ShareSafe is the first platform that enables users to perform data perturbation, utility evaluation, De-A evaluation, and Privacy Quantification. Leveraging ShareSafe, we conduct a more comprehensive and advanced utility and privacy evaluation. The results demonstrate that (1) The risk of privacy leakage of anonymized graph increases with the attackers' background knowledge. (2) For a successful de-anonymization attack, the seed mapping, even relatively small, plays a much more important role than the auxiliary graph. (3) The structure of graph has a fundamental and significant effect on the utility and privacy of the graph. (4) There is no optimal anonymization/de-anonymization algorithm. For different environment, the performance of each algorithm varies from each other.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3383-3397
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• 2023

Scene graphs serve as semantic abstractions of images and play a crucial role in enhancing visual comprehension and reasoning. However, the performance of Scene Graph Generation is often compromised when working with biased data in real-world situations. While many existing systems focus on a single stage of learning for both feature extraction and classification, some employ Class-Balancing strategies, such as Re-weighting, Data Resampling, and Transfer Learning from head to tail. In this paper, we propose a novel approach that decouples the feature extraction and classification phases of the scene graph generation process. For feature extraction, we leverage a transformer-based architecture and design an adaptive calibration function specifically for predicate classification. This function enables us to dynamically adjust the classification scores for each predicate category. Additionally, we introduce a Distribution Alignment technique that effectively balances the class distribution after the feature extraction phase reaches a stable state, thereby facilitating the retraining of the classification head. Importantly, our Distribution Alignment strategy is model-independent and does not require additional supervision, making it applicable to a wide range of SGG models. Using the scene graph diagnostic toolkit on Visual Genome and several popular models, we achieved significant improvements over the previous state-of-the-art methods with our model. Compared to the TDE model, our model improved mR@100 by 70.5% for PredCls, by 84.0% for SGCls, and by 97.6% for SGDet tasks.

• The KIPS Transactions:PartC
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• v.15C no.6
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• pp.455-468
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• 2008

In this paper, we propose FAS model for establishing trust based on digital certificates in Grid security framework. The existing RBAC(Role Based Access Control) model is extended to provide permissions depending on the users‘ roles. The FAS model is designed for a system independent integrated Grid security by detailing and extending the fundamental architecture of user, role, and permission. FAS decides each user’s role, allocates access right, and publishes attribute certificate. FAS is composed of three modules: RDM, PCM, and CCM. The RDM decides roles of the user during trust negotiation process and improves the existing low level Grid security in which every single user maps a single shared local name. Both PCM and CCM confirm the capability of the user based on various policies that can restrict priority of the different user groups and roles. We have analyzed the FAS strategy with the complexity of the policy graph-based strategy. In particular, we focused on the algorithm for constructing the policy graph. As a result, the total running time was significantly reduced.