In public clouds such as Amazon EC2, there are two main pricing models in purchasing Infrastructure-as-a-Service (IaaS) instances: the pay-as-you-go model and the subscription model. For these two options, users can dynamically combine them to provide services for demands to save their instance acquisition costs. Making optimal decisions toward the purchase of IaaS instances generally requires prior knowledge of future demands; however, it is difficult for users to predict all future workloads accurately. To deal with this problem, online reservation algorithms have been proposed to guide users in reserving instances. However, existing online algorithms do not conform to the pricing rules currently used in public cloud platforms. Therefore, we put forward a new online reserving algorithm for instance in accordance with the pricing policies used in most public IaaS offerings. Specifically, in this study, we use Amazon EC2 as an example to illustrate our algorithm. Through theoretical analysis, we prove that the cost of the proposed algorithm A _β in this paper is not greater than 2-1/β times of the optimal offline algorithm, where β>1 is a critical point in the online reservation algorithm proposed in this paper. Via extensive experimental simulations using both synthetic and actual workload datasets, we demonstrated that the online algorithm A _β is much more cost effective for cloud users than always paying-as-you-go in public IaaS markets.

In the 6G era, Internet of Things (IoT) devices can form a blockchain network, which also faces the problems of data sharing. The data transmitted and stored through the network have the risk of privacy leaking. Encrypting the shared data can satisfy the need of the privacy, and retrieving the encrypted data can make the data used efficiently. However, to enable users to retrieve encrypted data and perform fine-grained authorization on their encrypted files is still a great challenge. Although attribute-based keyword search (ABKS) is a well-received solution to the challenge, there are still privacy and efficiency issues if the traditional ABKS schemes are directly used in blockchain data sharing. In order to solve the problems, this article proposes privacy protection data retrieval scheme with an inverted index, which is an application of attribute-based encryption. First, our scheme is proved secure against the outside keyword guessing attack (KGA) and chosen keyword attack (CKA) under the semitrusted model. Second, the scheme returns a multikeywords ranked result. Third, we analyze the efficiency of our scheme and verify it by simulation. The results show that our scheme has improvement in efficiency and can meet the data sharing needs of the blockchain network composed of IoT devices.

Telecare medical information systems (TMIS) enable patients to access healthcare delivery services conveniently. With the explosive development occurring in cloud computing and services, storage of personal medical and health information outsourcing to cloud infrastructure has been a potential alternative. However, this has entailed many considerable security and privacy issues. In order to address the security loopholes, we propose a promising solution satisfying the requirements of cloud computing scenarios for telemedical systems. The proposed scheme could provide both data confidentiality and message authenticity while preserving anonymity. Furthermore, the formal security proof demonstrates that the proposed scheme is resistant to various attacks. The performance comparisons show the proposal’s workability and it is well suited to adoption in telemedical services.

Internet of Things (IoT) and cloud computing are adopted widely in daily life and industrial production. Sensors of IoT equipment gather personal, sensitive and important data, which is stored in a cloud server. The cloud helps users to save cost and collaborate. However, the privacy of data is also at risk. Public-key encryption with keyword search (PEKS) is convenient for users to use the data without leaking privacy. In this article, we give a scheme of PEKS for a multi-user to realize the multi-keyword search at once and extend it to show a rank based on keywords match. The receiver can finish the search by himself or herself. With private cloud and server cloud, most users’ computing can be outsourced. Moreover, the PEKS can be transferred to a multi-user model in which the private cloud is used to manage receivers and outsource. The store cloud and the private cloud both obtain nothing with the keyword information. Then our IoT devices can easily run these protocols. As we do not use any pairing operations, the scheme is under more general assumptions that means the devices do not need to take on the heavy task of calculating pairing.

Aim. The purpose of this study is how to better detect attack traffic in imbalance datasets. The deep learning technology has played an important role in detecting malicious network traffic in recent years. However, it suffers serious imbalance distribution of data if the traffic model skews towards the modeling in the benign direction, because only a small portion of traffic is malicious, while most network traffic is benign. That is the reason why the authors wrote this manuscript. Methods. We propose a cost-sensitive approach to improve the HTTP traffic detection performance with imbalanced data and also present a character-level abstract feature extraction approach that can provide features with clear decision boundaries in addition. Finally, we design a spark-based HTTP traffic detection system based on these two approaches. Results. The methods proposed in this paper work well in imbalanced datasets. Compared to other methods, the experiment results indicate that our system has F1-score in a high precision. Conclusion. For imbalanced HTTP traffic detection, we confirmed that the method of feature extraction and the cost function is very effective. In the future, we may focus on how to use the cost function to further improve detection performance.