• 정보보호학회논문지
• /
• 제21권6호
• /
• pp.55-65
• /
• 2011

최근 유무선 통신 네트워크의 확산 및 고속화에 따라 인터넷 기술을 활용한 높은 수준의 확장성을 제공하는 클라우드 컴퓨팅 서비스(Cloud Computing Service) 이용이 증가하고 있다. 클라우드 컴퓨팅 서비스란 네트워크, 서버, 스토리지, 응용프로그램 등 다양한 컴퓨팅 자원들의 공유된 풀에 네트워크로 접근하여 언제든지 편리하게 사용 가능한 컴퓨팅 방식으로써 컴퓨팅 환경의 가상화라는 클라우드 컴퓨팅 서비스의 본질적인 특성으로 인해 디지털 포렌식 관점에서 사건 수사 시 데이터를 확보하는 일 자체가 어려운 현실에 직면했다. 본 논문에서는 클라우드 컴퓨팅 서비스에 대한 디지털 포렌식 관점의 연구와 IaaS 형태의 클라우드 컴퓨팅서비스 중 시장 점유율의 대부분을 차지하고 있는 AWS(Amazon Web Service)와 Rackspace에 대한 증거데이터 수집 및 분석방안을 제시한다.

• 융합보안논문지
• /
• 제15권2호
• /
• pp.63-68
• /
• 2015

현재의 ICT 인프라(인터넷과 서버/Client 연동)는 다양한 장치, 서비스, 비즈니스 및 기술 진화에 따른 신속한 대응에 어려움을 겪고 있다. 클라우드 컴퓨팅(Cloud Computing)은 구름 같은 네트워크 환경에서 원하는 작업을 요청하여 실행한다는 데서 기원하였으며, 인터넷 기술을 활용하여 IT 자원을 서비스로 제공하는 컴퓨팅을 뜻하고 오늘날 IT 트렌드의 하나로 가장 주목 받고 있다. 이러한 분산클라우드 환경에서는 네트워크 및 컴퓨팅 자원에 대한 통합 관리 체계를 통하여 관리 비용 증가 문제를 원천적으로 해결하고 분산된 마이크로 데이터센터(Micro DC(Data Center))를 통하여 코어 네트워크 트래픽 폭증 문제를 해결하여 비용 절감 효과를 높일 수 있다. 그러나 기존의 Flooding 방식은 인접한 모든 DC들에게 전송하기 때문에 많은 트래픽을 유발 할 수 있다. 이를 위해 Restricted Path Flooding 알고리즘이 제안되었으나 대규모 네트워크에서는 여전히 트래픽을 발생할 수 있는 단점이 있어서 본 논문에서는 홉수 제한을 통하여 이를 개선한 Lightweight Path Flooding 알고리즘을 제안하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제15권12호
• /
• pp.4345-4363
• /
• 2021

Deep Learning as a Service (DLaaS), utilizing the cloud-based deep neural network models to provide customer prediction services, has been widely deployed on mobile cloud computing (MCC). Such services raise privacy concerns since customers need to send private data to untrusted service providers. In this paper, we devote ourselves to building an efficient protocol to classify users' images using the convolutional neural network (CNN) model trained and held by the server, while keeping both parties' data secure. Most previous solutions commonly employ homomorphic encryption schemes based on Ring Learning with Errors (RLWE) hardness or two-party secure computation protocols to achieve it. However, they have limitations on large communication overheads and costs in MCC. To address this issue, we present LeHE4SCNN, a scalable privacy-preserving and communication-efficient framework for CNN-based DLaaS. Firstly, we design a novel low-expansion rate homomorphic encryption scheme with packing and unpacking methods (LeHE). It supports fast homomorphic operations such as vector-matrix multiplication and addition. Then we propose a secure prediction framework for CNN. It employs the LeHE scheme to compute linear layers while exploiting the data shuffling technique to perform non-linear operations. Finally, we implement and evaluate LeHE4SCNN with various CNN models on a real-world dataset. Experimental results demonstrate the effectiveness and superiority of the LeHE4SCNN framework in terms of response time, usage cost, and communication overhead compared to the state-of-the-art methods in the mobile cloud computing environment.

• 한국정보통신학회:학술대회논문집
• /
• 한국정보통신학회 2013년도 추계학술대회
• /
• pp.435-437
• /
• 2013

This paper proposes a system composed of wireless sensor network and cloud to monitor storage environment of pharmaceutical products. Integration of sensor networks to cloud is an emerging architecture offering the benefits of internet for monitoring to be done easily and remotely from anywhere and anytime and at the same time freeing the sensor network from processing, analysis, computational and storage of sensor data.

• International Journal of Computer Science & Network Security
• /
• 제21권7호
• /
• pp.43-55
• /
• 2021

The amount of Big Data generated from multiple sources is continuously increasing. Traditional storage methods lack the capacity for such massive amounts of data. Consequently, most organizations have shifted to the use of cloud storage as an alternative option to store Big Data. Despite the significant developments in cloud storage, it still faces many challenges, such as privacy and security concerns. This paper discusses Big Data, its challenges, and different classifications of security and privacy challenges. Furthermore, it proposes a new classification of Big Data security and privacy challenges and offers some perspectives to provide solutions to these challenges.

• International Journal of Computer Science & Network Security
• /
• 제21권6호
• /
• pp.35-46
• /
• 2021

Small and Medium Enterprises (SMEs) are increasingly using ERP systems to connect and manage all their functions, whether internally between the different departments, or externally with customers in electronic commerce. However, the selection of the right ERP system is usually an issue, due to the complexities of identifying the criteria, weighting them, and selecting the best system and provider. Because cost is usually important for SMEs, ERP systems based on Cloud Software as a Service (SaaS) has been adopted by many SMEs. However, SMEs face an issue of selecting the right system. Therefore, this paper proposes a fuzziness ranking engine system in order to match the SMEs requirements with the most suitable service provider. The extensive experimental result shows that our approach has better result compared with traditional approaches.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제15권3호
• /
• pp.1174-1192
• /
• 2021

With the assistance of non-orthogonal multiple access (NOMA), the spectrum efficiency and the number of users in cloud radio access network (CRAN) can be greatly improved. In this paper, the system performance of NOMA-assisted CRAN is investigated. Specially, the outage probability (OP) and ergodic sum rate (ESR), are derived for performance evaluation of the system, respectively. Based on this, by minimizing the OP of the system, a suboptimal power allocation (PA) scheme with closed-form PA coefficients is proposed. Numerical simulations validate the accuracy of the theoretical results, where the derived OP has more accuracy than the existing one. Moreover, the developed PA scheme has superior performance over the conventional fixed PA scheme but has smaller performance loss than the optimal PA scheme using the exhaustive search method.

• Journal of Information Processing Systems
• /
• 제20권3호
• /
• pp.360-374
• /
• 2024

A personalized course recommendation algorithm based on deep learning in an online education cloud platform is proposed to address the challenges associated with effective information extraction and insufficient feature extraction. First, the user potential preferences are obtained through the course summary, course review information, user course history, and other data. Second, by embedding, the word vector is turned into a low-dimensional and dense real-valued vector, which is then fed into the compressed interaction network-deep neural network model. Finally, considering that learners and different interactive courses play different roles in the final recommendation and prediction results, an attention mechanism is introduced. The accuracy, recall rate, and F1 value of the proposed method are 0.851, 0.856, and 0.853, respectively, when the length of the recommendation list K is 35. Consequently, the proposed strategy outperforms the comparison model in terms of recommending customized course resources.

• International Journal of Computer Science & Network Security
• /
• 제24권8호
• /
• pp.105-118
• /
• 2024

The decision to integrate mobile cloud computing (MCC) in higher education without first defining suitable usage scenarios is a global issue as the usage of such services becomes extensive. Consequently, this study investigates the security determinants of the educational use of mobile cloud computing among universities students. This study proposes and develops a theoretical model by adopting and modifying the Protection Motivation Theory (PMT). The studys findings show that a significant amount of variance in MCC adoption was explained by the proposed model. MCC adoption intention was shown to be highly influenced by threat appraisal and coping appraisal factors. Perceived severity alone explains 37.8% of students "Intention" to adopt MCC applications, which indicates the student's perception of the degree of harm that would happen can hinder them from using MCC. It encompasses concerns about data security, privacy breaches, and academic integrity issues. Response cost, perceived vulnerability and response efficacy also have significant influence on students "intention" by 18.8%, 17.7%, and 6.7%, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제9권11호
• /
• pp.4268-4290
• /
• 2015

Future mobile communications face enormous challenges as traditional voice services are replaced with increasing mobile multimedia and data services. To address the vast data traffic volume and the requirement of user Quality of Experience (QoE) in the next generation mobile networks, it is imperative to develop efficient content distribution technique, aiming at significantly reducing redundant data transmissions and improving content delivery performance. On the other hand, in recent years cloud computing as a promising new content-centric paradigm is exploited to fulfil the multimedia requirements by provisioning data and computing resources on demand. In this paper, we propose a cooperative caching framework which implements State based Content Distribution (SCD) algorithm for future mobile networks. In our proposed framework, cloud service providers deploy a plurality of cloudlets in the network forming a Distributed Cloud Service Network (DCSN), and pre-allocate content services in local cloudlets to avoid redundant content transmissions. We use content popularity and content state which is determined by content requests, editorial updates and new arrivals to formulate a content distribution optimization model. Data contents are deployed in local cloudlets according to the optimal solution to achieve the lowest average content delivery latency. We use simulation experiments to validate the effectiveness of our proposed framework. Numerical results show that the proposed framework can significantly improve content cache hit rate, reduce content delivery latency and outbound traffic volume in comparison with known existing caching strategies.