• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.3
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• pp.487-498
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• 2023

Security incidents using vulnerabilities in cloud services occur, but it is difficult to collect and analyze traces of incidents in cloud environments with complex and diverse service models. As a result, the importance of cloud forensics research has emerged, and infringement response scenarios must be designed from the perspective of cloud service users (CSUs) and cloud service providers (CSPs) based on representative security threat cases in the public cloud service model. This simulated hacking-based proactive cloud infringement response framework can be used to respond to the cloud service critical resource attack process from the viewpoint of vulnerability detection before cyberattacks occur on the cloud, and can also be expected for data acquisition. Therefore, in this paper, we propose a framework for preventive cloud infringement based on simulated hacking by analyzing and utilizing Cloudfox, a cloud penetration test tool.

• Journal of Computing Science and Engineering
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• v.9 no.3
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• pp.119-133
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• 2015

While cloud computing is gaining popularity, diverse security and privacy issues are emerging that hinder the rapid adoption of this new computing paradigm. And the development of defensive solutions is lagging behind. To ensure a secure and trustworthy cloud environment it is essential to identify the limitations of existing solutions and envision directions for future research. In this paper, we have surveyed critical security and privacy challenges in cloud computing, categorized diverse existing solutions, compared their strengths and limitations, and envisioned future research directions.

• Journal of Korea Multimedia Society
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• v.21 no.8
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• pp.909-916
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• 2018

Cloud storage services have many advantages. As a result, the amount of data stored in the storage of the cloud service provider is increasing rapidly. This increase in demand forces cloud storage providers to apply deduplication technology for efficient use of storages. However, deduplication technology has inherent security and privacy concerns. Several schemes have been proposed to solve these problems, but there are still some vulnerabilities to well-known attacks on deduplication techniques. In this paper, we examine some of the existing schemes and analyze their security weaknesses.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5653-5672
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• 2019

In cloud computing era, an increasing number of resource-constrained users outsource their data to cloud servers. Due to the untrustworthiness of cloud servers, it is important to ensure the integrity of outsourced data. However, most of existing solutions still have challenging issues needing to be addressed, such as the identity privacy protection of users, the traceability of users, the supporting of dynamic user operations, and the publicity of auditing. In order to tackle these issues simultaneously, in this paper, we propose a traceable dynamic public auditing scheme with identity privacy preserving for cloud storage. In the proposed scheme, a single user, including a group manager, is unable to know the signer's identity. Furthermore, our scheme realizes traceability based on a secret sharing mechanism and supports dynamic user operations. Based on the security and efficiency analysis, it is shown that our scheme is secure and efficient.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.342-347
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• 2022

On Cloud, the important data of the user that is protected on remote servers can be accessed via internet. Due to rapid shift in technology nowadays, there is a swift increase in the confidential and pivotal data. This comes up with the requirement of data security of the user's data. Data is of different type and each need discrete degree of conservation. The idea of data security data science permits building the computing procedure more applicable and bright as compared to conventional ones in the estate of data security. Our focus with this paper is to enhance the safety of data on the cloud and also to obliterate the problems associated with the data security. In our suggested plan, some basic solutions of security like cryptographic techniques and authentication are allotted in cloud computing world. This paper put your heads together about how machine learning techniques is used in data security in both offensive and defensive ventures, including analysis on cyber-attacks focused at machine learning techniques. The machine learning technique is based on the Supervised, UnSupervised, Semi-Supervised and Reinforcement Learning. Although numerous research has been done on this topic but in reference with the future scope a lot more investigation is required to be carried out in this field to determine how the data can be secured more firmly on cloud in respect with the Machine Learning Techniques and cryptographic methods.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.4
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• pp.775-783
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• 2017

As interest in cloud computing grows, the number of users using cloud computing services is increasing. However, cloud computing technology has been steadily challenged by security concerns. Therefore, various security breaches are springing up to enhance the system security for cloud services users. In particular, research on detection of malicious VM (Virtual Machine) is actively underway through the introspecting virtual machines on the cloud platform. However, memory analysis technology is not used as a monitoring tool in the environments where multiple virtual machines are run on a single server platform due to obstructive monitoring overhead. As a remedy to the challenging issue, we proposes a computationally efficient instance memory introspection scheme to minimize the overhead that occurs in memory dump and monitor it through a partial memory monitoring based on the well-defined kernel memory map library.

• Convergence Security Journal
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• v.17 no.3
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• pp.15-20
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• 2017

Cloud computing is a service that to use IT resources (software, storage, server, network) through various equipment in an Internet-enabled environment. Due to convenience, efficiency, and cost reduction, the utilization rate has increased recently. However, Cloud providers have become targets for attack Also, Abuse of cloud service is considered as the top security threat. The existing digital forensic procedures are suitable for investigations on individual terminals. In this paper, we propose a new investigation model by analyzing the vulnerable points that occur when you investigate the cloud environment with the existing digital forensic investigation procedure. The proposed investigation model adds a way to obtain account information, and can apply public cloud and private cloud together. Cloud services are also easily accessible and are likely to destroy digital evidence. Therefore, the investigation model was reinforced by adding an account access blocking step.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.3
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• pp.579-589
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• 2017

The cloud environment is hypervisor-based, and many virtual machines are interconnected, which makes propagation of malicious code easier than other environments. Accordingly, this paper proposes a malicious traffic dynamic analysis system for secure cloud environment. The proposed system continuously monitors and analyzes malicious activity in an isolated virtual network environment by distinguishing malicious traffic that occurs in a cloud environment. In addition, the analyzed results are reflected in the distinguishment and analysis of malicious traffic that occurs in the future. The goal of this research is secure and efficient malicious traffic dynamic analysis by constructing the malicious traffic analysis environment in the cloud environment for detecting and responding to the new and variant malicious traffic generated in the cloud environment.

• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.61-70
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• 2024

Cloud Computing is internet-based computing, where the users are provided with whatever service they need from the resources, software, and information. Recently, the security of cloud computing is considered as one of the major issues for both cloud service providers CSP and end-users. Privacy and highly confidential data make many users refuse to store their data within cloud computing, since data on cloud computing is not dully secured. The cryptographic algorithm is a technique which is used to maintain the security and privacy of the data on the cloud. In this research, we applied eight different cryptographic algorithms on Xen and KVM as hypervisors on cloud computing, to be able to measure and compare the performance of the two hypervisors. Response time and CPU utilization while encryption and decryption have been our aspects to measure the performance. In terms of response time and CPU utilization, results show that KVM is more efficient than Xen on average at 11.5% and 11% respectively. While TripleDES cryptographic algorithm shows a more efficient time response at Xen hypervisor than KVM.

• Advances in nano research
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• v.15 no.6
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• pp.513-520
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• 2023

The exponential proliferation of cutting-edge computing technologies has spurred organizations to outsource their data and computational needs. In the realm of cloud-based computing environments, ensuring robust security, encompassing principles such as confidentiality, availability, and integrity, stands as an overarching imperative. Elevating security measures beyond conventional strategies hinges on a profound comprehension of malware's multifaceted behavioral landscape. This paper presents an innovative paradigm aimed at empowering cloud service providers to adeptly model user behaviors. Our approach harnesses the power of a Particle Swarm Optimization-based Probabilistic Neural Network (PSO-PNN) for detection and recognition processes. Within the initial recognition module, user behaviors are translated into a comprehensible format, and the identification of malicious nano-structures behaviors is orchestrated through a multi-layer neural network. Leveraging the UNSW-NB15 dataset, we meticulously validate our approach, effectively characterizing diverse manifestations of malicious nano-structures behaviors exhibited by users. The experimental results unequivocally underscore the promise of our method in fortifying security monitoring and the discernment of malicious nano-structures behaviors.