• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.9
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• pp.3365-3383
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• 2021

The rapid growth of data has successfully promoted the development of modern information and communication technologies, which are used to process data generated by public urban departments and citizens in modern cities. In specific application areas where the ciphertext of messages generated by different users' needs to be transmitted, the concept of broadcast encryption is important. It can not only improve the transmission efficiency but also reduce the cost. However, the existing schemes cannot entirely ensure the privacy of receivers and dynamically adjust the user authorization. To mitigate these deficiencies, we propose an efficient, secure identity-based broadcast encryption scheme that achieves direct revocation and receiver anonymity, along with the analysis of smart grid solutions. Moreover, we constructed a security model to ensure wireless data transmission under cloud computing and internet of things integrated devices. The achieved results reveal that the proposed scheme is semantically secure in the random oracle model. The performance of the proposed scheme is evaluated through theoretical analysis and numerical experiments.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.17-22
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• 2022

In this paper, we design a lightweight embedded device that can support intelligent edge computing, and show that the device quickly detects an object in an image input from a camera device in real time. The proposed system can be applied to environments without pre-installed infrastructure, such as an intelligent video control system for industrial sites or military areas, or video security systems mounted on autonomous vehicles such as drones. The On-Device AI(Artificial intelligence) technology is increasingly required for the widespread application of intelligent vision recognition systems. Computing offloading from an image data acquisition device to a nearby edge device enables fast service with less network and system resources than AI services performed in the cloud. In addition, it is expected to be safely applied to various industries as it can reduce the attack surface vulnerable to various hacking attacks and minimize the disclosure of sensitive data.

• Smart Media Journal
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• v.12 no.2
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• pp.66-75
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• 2023

Recently, the number of cloud web applications is increasing owing to the accelerated migration of enterprises and public sector information systems to the cloud. Traditional network attacks on cloud web applications are characterized by Denial of Service (DoS) attacks, which consume network resources with a large number of packets. However, HTTP DoS attacks, which consume application resources, are also increasing recently; as such, developing security technologies to prevent them is necessary. In particular, since low-bandwidth HTTP DoS attacks do not consume network resources, they are difficult to identify using traditional security solutions that monitor network metrics. In this paper, we propose a new detection model for detecting HTTP DoS attacks on cloud web applications by collecting the application metrics of web servers and learning them using machine learning. We collected 18 types of application metrics from an Apache web server and used five machine learning and two deep learning models to train the collected data. Further, we confirmed the superiority of the application metrics-based machine learning model by collecting and training 6 additional network metrics and comparing their performance with the proposed models. Among HTTP DoS attacks, we injected the RUDY and HULK attacks, which are low- and high-bandwidth attacks, respectively. As a result of detecting these two attacks using the proposed model, we found out that the F1 scores of the application metrics-based machine learning model were about 0.3 and 0.1 higher than that of the network metrics-based model, respectively.

• Journal of Korea Society of Industrial Information Systems
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• v.17 no.1
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• pp.73-93
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• 2012

The primary objective of this research is to explain how to implement the Service Oriented Architecture (hereafter SOA) enabled Enterprise Resource Planning (hereafter ERP) system successfully for service industries. An implementation of the ERP system help many organizations to alleviate the difficult job of supporting inflexible or legacy systems that in most cases result in cost increases, data redundancy and inaccuracy, and various inefficiencies. However, the ERP system is losing its market share rapidly to the cloud computing system which utilizes the Software-as-a-service (hereafter SaaS) and SOA. The SOA is an approach to integrate various types of IT resources to leverage existing ERP system, while at the same time building an infrastructure that can readily respond to new business environment and offer new dynamic applications. The companies that implement this system have less of a need for the kinds of all-in-one ERP system that have dominated the back office for decades and can move freely to best-of-breed applications. This research will identify the benefits and costs of the SOA enabled ERP system through case studies and its impact on competitive priorities such as cost, quality, delivery, and flexibility.

• Journal of Multimedia Information System
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• v.9 no.1
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• pp.11-20
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• 2022

In this paper, we have recently created self-driving cars and self-parking systems in human-friendly cars that can provide high safety and high convenience functions by recognizing the internal and external situations of automobiles in real time by incorporating next-generation electronics, information communication, and function control technologies. And with the development of connected cars, the ITS (Intelligent Transportation Systems) market is expected to grow rapidly. Intelligent Transportation System (ITS) is an intelligent transportation system that incorporates technologies such as electronics, information, communication, and control into the transportation system, and aims to implement a next-generation transportation system suitable for the information society. By combining the technologies of connected cars and Internet of Things with software features and operating systems, future cars will serve as a service platform to connect the surrounding infrastructure on their own. This study creates a research methodology based on the Enhanced Security Model in Self-Driving Cars model. As for the types of attacks, Availability Attack, Man in the Middle Attack, Imperial Password Use, and Use Inclusive Access Control attack defense methodology are used. Along with the commercialization of 5G, various service models using advanced technologies such as autonomous vehicles, traffic information sharing systems using IoT, and AI-based mobility services are also appearing, and the growth of smart transportation is accelerating. Therefore, research was conducted to defend against hacking based on vulnerabilities of smart cars based on artificial intelligence blockchain.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.2
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• pp.786-799
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• 2018

Recently, NFV has attracted attention as a next-generation network virtualization technology for hardware -independent and efficient utilization of resources. NFV is a technology that not only virtualize computing, server, storage, network resources based on cloud computing but also connect Multi-Tenant of VNFs, a software network function. Therefore, it is possible to reduce the cost for constructing a physical network and to construct a logical network quickly by using NFV. However, in NFV, when a new VNF is added to a running Tenant, authentication between VNFs is not performed. Because of this problem, it is impossible to identify the presence of Fake-VNF in the tenant. Such a problem can cause an access from malicious attacker to one of VNFs in tenant as well as other VNFs in the tenant, disabling the NFV environment. In this paper, we propose Auto-configurable Security Mechanism in NFV including authentication between tenant-internal VNFs, and enforcement mechanism of security policy for traffic control between VNFs. This proposal not only authenticate identification of VNF when the VNF is registered, but also apply the security policy automatically to prevent malicious behavior in the tenant. Therefore, we can establish an independent communication channel for VNFs and guarantee a secure NFV environment.

• Journal of Internet Computing and Services
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• v.19 no.2
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• pp.33-41
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• 2018

In recent years, technology is rapidly changing to support new service consumption and distribution models in multimedia service systems and hybrid delivery of media services is a key factor for enabling next generation multimedia services. This phenomenon can lead to rapidly increasing network traffic and ultimately has a direct and aggravating effect on the user's quality of service (QOS). To address the issue, we propose a novel system architecture to provide smart hybrid media services efficiently. The architecture is designed to apply the software-defined networking (SDN) method, detect changes in traffic, and combine the data, including user data, service features, and computation node status, to provide a service schedule that is suitable for the current state. To this end, the proposed architecture is based on 2-level scheduling, where Level-1 scheduling is responsible for the best network path and a computation node for processing the user request, whereas Level-2 scheduling deals with individual service requests that arrived at the computation node. This paper describes the overall concept of the architecture, as well as the functions of each component. In addition, this paper describes potential scenarios that demonstrate how this architecture could provide services more efficiently than current media-service architectures.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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• pp.874-890
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• 2021

With the widespread deployment of the fifth-generation (5G) communication networks, various real-time applications are rapidly increasing and generating massive traffic on backhaul network environments. In this scenario, network congestion will occur when the communication and computation resources exceed the maximum available capacity, which severely degrades the network performance. To alleviate this problem, this paper proposed an intelligent resource allocation (IRA) to integrate with the extant resource adjustment (ERA) approach mainly based on the convergence of support vector machine (SVM) algorithm, software-defined networking (SDN), and mobile edge computing (MEC) paradigms. The proposed scheme acquires predictable schedules to adapt the downlink (DL) transmission towards off-peak hour intervals as a predominant priority. Accordingly, the peak hour bandwidth resources for serving real-time uplink (UL) transmission enlarge its capacity for a variety of mission-critical applications. Furthermore, to advance and boost gateway computation resources, MEC servers are implemented and integrated with the proposed scheme in this study. In the conclusive simulation results, the performance evaluation analyzes and compares the proposed scheme with the conventional approach over a variety of QoS metrics including network delay, jitter, packet drop ratio, packet delivery ratio, and throughput.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.3970-3990
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• 2021

In view of the problems of insufficient data collection and processing capability of multi-source heterogeneous equipment, and low visibility of equipment status at the ship block construction site. A data collection method for ship block construction equipment based on wireless sensor network (WSN) technology and a data processing method based on edge computing were proposed. Based on the Browser/Server (B/S) architecture and the OneNET platform, an online monitoring system for ship block construction equipment was designed and developed, which realized the visual online monitoring and management of the ship block construction equipment status. Not only that, the feasibility and reliability of the monitoring system were verified by using the intelligent tire frame system as the application object. The research of this project can lay the foundation for the ship block construction equipment management and the ship block intelligent construction, and ultimately improve the quality and efficiency of ship block construction.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.270-278
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• 2023

We propose a container orchestration system for process workloads that combines the potential of big data and machine learning technologies to integrate enterprise process-centric workloads. This proposed system analyzes big data generated from industrial automation to identify hidden patterns and build a machine learning prediction model. For each machine learning case, training data is loaded into a data store and preprocessed for model training. In the next step, you can use the training data to select and apply an appropriate model. Then evaluate the model using the following test data: This step is called model construction and can be performed in a deployment framework. Additionally, a visual hierarchy is constructed to display prediction results and facilitate big data analysis. In order to implement parallel computing of PCA in the proposed system, several virtual systems were implemented to build the cluster required for the big data cluster. The implementation for evaluation and analysis built the necessary clusters by creating multiple virtual machines in a big data cluster to implement parallel computation of PCA. The proposed system is modeled as layers of individual components that can be connected together. The advantage of a system is that components can be added, replaced, or reused without affecting the rest of the system.