• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.1
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• pp.124-130
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• 2007

RFID technology is considered as a promising solution to increase efficiency of port logistics. Especially active RFID technology, such as e-Seal for container security, is receiving attention nowadays. If active RFID system is combined with passive RFID and legacy bar-code system overall efficiency of port logistics can be improved However, due to the device dependent control interface of RFID readers, there are many difficulties in making active-passive combined RFID system environment. In this paper, we introduce Smart Reader Interface (SRI) system, which provides a device independent RFID reader interrace to control different kinds of RFID readers by hiding device dependent control interface through adapter architecture which is similar to device driver of conventional operating systems. The key design objectives of SRI are the followings; conformance to the related standard. efficiency in processing, easy addition of an adapter for a new RFID reader. Actually, the implemented SRI system can support various kinds of commercial RFID readers, and through the test carried out not only in laboratory but also in the container terminal in the GwangYang Port, its practicality is verified.

• Journal of IKEEE
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• v.25 no.3
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• pp.571-577
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• 2021

This paper describes ISO/TR 4804, an international standard to describe how to design and verify autonomous cars to ensure safety and cybersecurity. Goals of ISO/TR 4804 are (1) positive risk balance and (2) avoidance of unreasonable risk. It also 12 principles of safety and cybersecurity to achieve these goals. In the design procedures, it describes (1) 13 capabilities to achieve these safety and cybersecurity principles, (2) hardware and software elements to achieve these capabilities, and (3) a generic logical architecture to combine these elements. In the verification procedures, it describes (1) 5 challenges to ensure safety and cybersecurity, (2) test goals, platforms, and solutions to achieve these challenges, (3) simulation and field operation methods, and (4) verification methods for hardware and software elements. Especially, it regards deep neural network as a software component and it describe design and verification methods of autonomous cars.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.558-579
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• 2021

The cloud storage provides flexible data storage services for data owners to remotely outsource their data, and reduces data storage operations and management costs for data owners. These outsourced data bring data security concerns to the data owner due to malicious deletion or corruption by the cloud service provider. Data integrity verification is an important way to check outsourced data integrity. However, the existing data verification schemes only consider the case that a verifier launches multiple data verification challenges, and neglect the verification overhead of multiple data verification challenges launched by multiple verifiers at a similar time. In this case, the duplicate data in multiple challenges are verified repeatedly so that verification resources are consumed in vain. We propose a duplicate data verification algorithm based on multiple verifiers and multiple challenges to reduce the verification overhead. The algorithm dynamically schedules the multiple verifiers' challenges based on verification time and the frequent itemsets of duplicate verification data in challenge sets by applying FP-Growth algorithm, and computes the batch proofs of frequent itemsets. Then the challenges are split into two parts, i.e., duplicate data and unique data according to the results of data extraction. Finally, the proofs of duplicate data and unique data are computed and combined to generate a complete proof of every original challenge. Theoretical analysis and experiment evaluation show that the algorithm reduces the verification cost and ensures the correctness of the data integrity verification by flexible batch data verification.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.1
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• pp.13-19
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• 2019

The accuracy of machine learning is greatly affected by amount of learning data and quality of data. Collecting existing Web-based learning data has danger that data unrelated to actual learning can be collected, and it is impossible to secure data transparency. In this paper, we propose a method for collecting data directly in parallel by blocks in a block - chain structure, and comparing the data collected by each block with data in other blocks to select only good data. In the proposed system, each block shares data with each other through a chain of blocks, utilizes the All-reduce structure of Parallel-SGD to select only good quality data through comparison with other block data to construct a learning data set. Also, in order to verify the performance of the proposed architecture, we verify that the original image is only good data among the modulated images using the existing benchmark data set.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.23 no.4
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• pp.204-217
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• 2018

This study proposes a cost-effective method to monitor coastal environments using e-Navigation-implemented domestic and international ferries, and to analyze big data of records such as wind, temperature, salinity, waves, and currents that are gathered through e-Navigation system. First, we present the concept and architecture of e-Navigation operation system based on the General Information Center on Maritime Safety and Security. Then, the marine observation system that can be applied to ferries operating in our nation's territory is discussed. Analytical methods, such as spatio-temporal mixed effects model, ensemble method, and meshfree method, in handling real-time big data obtained by the e-Navigation observing system are then explained in detail. This study will support the implementation of the Korean e-Navigation project that focuses on the safety of small vessels such as coasters and fishing vessels.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.5
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• pp.604-612
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• 2019

PKI Internet banking is used to have users register their public keys with the banking server together with the identity information, and verify the signature for both user and transaction authentications by using the registered public keys. Although the Blockchain-based financial systems such as Bitcoin adopt similar digital signature-based authentication scheme, there is no server that participants can register public keys with because they perform P2P payment transactions. The purpose of this paper is to identify the advantages and disadvantages of the Blockchain-based payment transactions by analyzing the differences between the most common PKI Internet banking and Blockchain payment systems. Based on the analysis, this paper suggests the issues that need to be enhanced from the aspects of architecture and security in order for Blockchain payment transaction systems to be applied universally.

• The Journal of Korean Institute of Information Technology
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• v.17 no.4
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• pp.109-117
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• 2019

In modern society, due to various accidents and crime threats committed to an unspecified number of people, individual security awareness is increasing throughout society and various surveillance techniques are being actively studied. Still, there is a decline in robustness due to many problems, requiring higher reliability monitoring techniques. Thus, this paper suggests a real-time change detection technique to complement the low robustness problem in various environments and dynamic/static change detection and to solve the cost efficiency problem. We used the Self-Organizing Map (SOM) applied as a data clustering technique to implement change detection, and we were able to confirm the superiority of noise robustness and abnormal detection judgment compared to the detection technique applied to the existing image surveillance system through simulation in the indoor office environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.565-586
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• 2022

Cloud storage's elastic expansibility not only provides flexible services for data owners to store their data remotely, but also reduces storage operation and management costs of their data sharing. The data outsourced remotely in the storage space of cloud service provider also brings data security concerns about data integrity. Data integrity verification has become an important technology for detecting the integrity of remote shared data. However, users without data access rights to verify the data integrity will cause unnecessary overhead to data owner and cloud service provider. Especially malicious users who constantly launch data integrity verification will greatly waste service resources. Since data owner is a consumer purchasing cloud services, he needs to bear both the cost of data storage and that of data verification. This paper proposes a verification control algorithm in data integrity verification for remotely outsourced data. It designs an attribute-based encryption verification control algorithm for multiple verifiers. Moreover, data owner and cloud service provider construct a common access structure together and generate a verification sentinel to verify the authority of verifiers according to the access structure. Finally, since cloud service provider cannot know the access structure and the sentry generation operation, it can only authenticate verifiers with satisfying access policy to verify the data integrity for the corresponding outsourced data. Theoretical analysis and experimental results show that the proposed algorithm achieves fine-grained access control to multiple verifiers for the data integrity verification.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.958-979
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• 2023

Due to laws, regulations, privacy, etc., between 70-90 percent of providers do not share medical data, forming a "data island". It is essential to collaborate across multiple institutions without sharing patient data. Most existing methods adopt distributed learning and centralized federal architecture to solve this problem, but there are problems of resource heterogeneity and data heterogeneity in the practical application process. This paper proposes a collaborative deep learning modelling method based on the blockchain network. The training process uses encryption parameters to replace the original remote source data transmission to protect privacy. Hyperledger Fabric blockchain is adopted to realize that the parties are not restricted by the third-party authoritative verification end. To a certain extent, the distrust and single point of failure caused by the centralized system are avoided. The aggregation algorithm uses the FedProx algorithm to solve the problem of device heterogeneity and data heterogeneity. The experiments show that the maximum improvement of segmentation accuracy in the collaborative training mode proposed in this paper is 11.179% compared to local training. In the sequential training mode, the average accuracy improvement is greater than 7%. In the parallel training mode, the average accuracy improvement is greater than 8%. The experimental results show that the model proposed in this paper can solve the current problem of centralized modelling of multicenter data. In particular, it provides ideas to solve privacy protection and break "data silos", and protects all data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.916-937
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• 2023

Most of the existing Distributed Denial-of-Service mitigation schemes in Software-Defined Networking are only implemented in the network domain managed by a single controller. In fact, the zombies for attackers to launch large-scale DDoS attacks are actually not in the same network domain. Therefore, abnormal traffic of DDoS attack will affect multiple paths and network domains. A single defense method is difficult to deal with large-scale DDoS attacks. The cooperative defense of multiple domains becomes an important means to effectively solve cross-domain DDoS attacks. We propose an efficient multi-domain DDoS cooperative defense mechanism by integrating blockchain and SDN architecture. It includes attack traceability, inter-domain information sharing and attack mitigation. In order to reduce the length of the marking path and shorten the traceability time, we propose an AS-level packet traceability method called ASPM. We propose an information sharing method across multiple domains based on blockchain and smart contract. It effectively solves the impact of DDoS illegal traffic on multiple domains. According to the traceability results, we designed a DDoS attack mitigation method by replacing the ACL list with the IP address black/gray list. The experimental results show that our ASPM traceability method requires less data packets, high traceability precision and low overhead. And blockchain-based inter-domain sharing scheme has low cost, high scalability and high security. Attack mitigation measures can prevent illegal data flow in a timely and efficient manner.