Building a Reputation Attack Detector for Effective Trust Evaluation in a Cloud Services Environment

Abstract

Cloud computing is a widely used technology that has changed the way people and organizations store and access information. This technology is versatile, and extensive amounts of data can be stored in the cloud. Businesses can access various services over the cloud without having to install applications. However, cloud computing services are provided over a public domain, which means that both trusted and non-trusted users can access the services. Although there are a number of advantages to cloud computing services, especially for business owners, various challenges are posed in terms of the privacy and security of information and online services. A threat that is widely faced in the cloud environment is the on/off attack, in which entities exhibit proper behavior for a given time period to develop a positive reputation and gather trust, after which they exhibit deception. Another threat often faced by trust management services is a collusion attack, which is also known as collusive malicious feedback behavior. This is carried out when a group of people work together to make false recommendations with the intention of damaging the reputation of another party, which is referred to as a slandering attack, or to enhance their own reputation, which is referred to as a self-promoting attack. In this paper, a viable solution is provided with the given trust model for preventing these attacks. This method works by providing effective security to cloud services by identifying malicious and inappropriate behaviors through the application of trust algorithms that can identify on/off attacks and collusion attacks by applying different security criteria. Finally, the results show that the proposed trust model system can provide high security by decreasing security risk and improving the quality of decisions of data owners and cloud operators.

Gaussian Flower Pollination Optimization Based Trusted Service Selection for Cloud Computing Environment

“…The performance comparison of GFPO-TS, fuzzy rule-based trust management (FRTM) [12] and reputation attack detector (RAD) [14] techniques have been conducted and the results are presented in this section.…”

“…Alshammari et al [14] have presented trust architecture for avoiding various attacks. By detecting malicious and doubtful actions using trust algorithms, it can recognize on/off and collusion attacks.…”

Reputation-based Security model for detecting biased attacks in BigData

“…This model helps to keep the trust as long as possible as compared to the existing trust models. Alshammaria and Alsubhi [13], they have proposed a trust model to identify biased attack using malicious nodes. In this model they have provided security to the services using trust algorithms that can identify the nodes.…”

Trust evaluation model for electric power mobile Internet environment based on graph and semantic time window

“…In addition, the parameters, such as the time window in continuous trust evaluation, are set manually and lack adaptivity [2][3][4][5][6][7]. At present, research on trust evaluation models mainly can be mainly divided into two categories based on multi-attribute evaluation models [8][9][10] and machine learning models [11], both of which attach importance to the mining of multidimensional historical behavioral evidence information of network entities and the continuity and adaptiveness of the trust evaluation process. However, these methods lack the study of historical behavioral data time windows.…”