Security Strategy for Virtual Machine Allocation in Cloud Computing

Jia, Hefei; Liu, Xu; Di, Xiaoqiang; Qi, Hui; Lin, Cong; Li, Jinqing; Yang, Huamin

doi:10.1016/j.procs.2019.01.204

Cited by 28 publications

(7 citation statements)

References 12 publications

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“…K-L Tsai et al presented the modified AES-based algorithm for power reduction in IoT using cloud computing applications [14]. In this paper, similarly, VM (virtual machine) allocation policy is used for security which is almost similar to the technique used in the previous work [36].…”

Section: Literature Reviewmentioning

confidence: 99%

Secure Framework Enhancing AES Algorithm in Cloud Computing

Awan

Shiraz

Hashmi

et al. 2020

Security and Communication Networks

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The tremendous growth of computational clouds has attracted and enabled intensive computation on resource-constrained client devices. Predominantly, smart mobiles are enabled to deploy data and computational intensive applications by leveraging on the demand service model of remote data centres. However, outsourcing personal and confidential data to the remote data servers is challenging for the reason of new issues involved in data privacy and security. Therefore, the traditional advanced encryption standard (AES) algorithm needs to be enhanced in order to cope with the emerging security threats in the cloud environment. This research presents a framework with key features including enhanced security and owner’s data privacy. It modifies the 128 AES algorithm to increase the speed of the encryption process, 1000 blocks per second, by the double round key feature. However, traditionally, there is a single round key with 800 blocks per second. The proposed algorithm involves less power consumption, better load balancing, and enhanced trust and resource management on the network. The proposed framework includes deployment of AES with 16, 32, 64, and 128 plain text bytes. Simulation results are visualized in a way that depicts suitability of the algorithm while achieving particular quality attributes. Results show that the proposed framework minimizes energy consumption by 14.43%, network usage by 11.53%, and delay by 15.67%. Hence, the proposed framework enhances security, minimizes resource utilization, and reduces delay while deploying services of computational clouds.

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Section: Literature Reviewmentioning

confidence: 99%

Secure Framework Enhancing AES Algorithm in Cloud Computing

Awan

Shiraz

Hashmi

et al. 2020

Security and Communication Networks

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“…Therefore, we give an estimation of the energy consumption by using a linear interpolation of the utilization change for a given time interval [10]. We adopt the definition from [24]. The formula is given in (9), where R k γ indicates the actual CPU proportion of the k th host under the allocation policy γ, N is the total number of hosts, and P max represents the maximum power that a host uses and 70% of power defines the minimum percentage of power a Host uses, even when it is if in idle mode.…”

Section: ) Performance Factorsmentioning

confidence: 99%

Interdependency Attack-Aware Secure and Performant Virtual Machine Allocation Policies With Low Attack Efficiency and Coverage

Sane,

Ba,

Fall

et al. 2024

IEEE Access

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Cloud computing has completely changed IT (information technology) by providing IT resources as services on the internet. However, certain types of attacks such as the interdependency attack are impeding its wide adoption. With the latter, an attacker, who succeeds in compromising the VM of a user, can traverse the hypervisor to launch an attack on the VM(s) of other users on the same hypervisor. Unfortunately, we note a lack of secure and performant allocation policies against this problem. In fact, existing policies focus on security but ignore other factors, including workload balance and energy consumption, which are vital for commercial cloud platforms. In this context, we propose different allocation policies for choosing the datacenter server to which we allocate a new virtual machine. These policies aim to minimize the interdependence of different users' VMs while keeping the system performant in terms of workload balance and/or power consumption. By default, our allocation policies treat all legitimate users as attackers and host their virtual machines according to their efficiency and coverage. We first design a secure and balanced solution that increases workload balance to avoid the servers from being overused. Afterward, we propose an algorithm that simultaneously addresses the objectives of security, power consumption, and workload balance. Based on our simulation results, our solutions perform better than existing algorithms when it comes to security, workload balance, and power consumption. The balanced solution reduces the chance of an attacker to zero and increases workload balance linearly. In other words, the workload balance is between [5, 35] and it utilizes slightly more hosts than existing proposals, with gains between [2, 8]. Although our final proposal is less secure than previous algorithms, it performs better, so it has a good workload balance ([5, 30]) and also consumes less energy.

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“…When using heuristic methods with the consideration of co-resident attacks, various policies were compared. Jia et al [23] proposed a VM allocation method to optimize load balancing and reduce energy consumption and security risks by managing CPU utilization of the hosting PMs. Miao et al offered two metrics to outline co-residency and conflict of the cloud [24].…”

Section: Related Work: Secure Cloud Resource Managementmentioning

confidence: 99%

“…An evolutionary mechanism was presented in the hierarchical cooperative game model for VMs deployment strategy to improve the efficiency in the public cloud environment. Jia et al modeled several basic VM allocation policies using game theory to achieve a quantitative analysis, while also presenting the attack effectiveness, coverage, power consumption, workload balance, and cost under the VM allocation policies and solving the mathematical solution in CloudSim [23]. Their results found that to reduce the efficiency rates for the attacker, the cloud provider should apply a probabilistic VM allocation policy.…”

Section: Related Work: Secure Cloud Resource Managementmentioning

confidence: 99%

Perspective Chapter: Deep Reinforcement Learning for Co-Resident Attack Mitigation in The Cloud

Cui¹,

Homsi²

2022

Artificial Intelligence

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Cloud computing brings convenience and cost efficiency to users, but multiplexing virtual machines (VMs) on a single physical machine (PM) results in various cybersecurity risks. For example, a co-resident attack could occur when malicious VMs use shared resources on the hosting PM to control or gain unauthorized access to other benign VMs. Most task schedulers do not contribute to both resource management and risk control. This article studies how to minimize the co-resident risks while optimizing the VM completion time through designing efficient VM allocation policies. A zero-trust threat model is defined with a set of co-resident risk mitigation parameters to support this argument and assume that all VMs are malicious. In order to reduce the chances of co-residency, deep reinforcement learning (DRL) is adopted to decide the VM allocation strategy. An effective cost function is developed to guide the reinforcement learning (RL) policy training. Compared with other traditional scheduling paradigms, the proposed system achieves plausible mitigation of co-resident attacks with a relatively small VM slowdown ratio.

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Security Strategy for Virtual Machine Allocation in Cloud Computing

Cited by 28 publications

References 12 publications

Secure Framework Enhancing AES Algorithm in Cloud Computing

Secure Framework Enhancing AES Algorithm in Cloud Computing

Interdependency Attack-Aware Secure and Performant Virtual Machine Allocation Policies With Low Attack Efficiency and Coverage

Perspective Chapter: Deep Reinforcement Learning for Co-Resident Attack Mitigation in The Cloud

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