REST APIs have a pivotal role in accessing protected resources. Despite the availability of security testing tools, mass assignment vulnerabilities are common in REST APIs, leading to unauthorized manipulation of sensitive data. We propose a lightweight approach to mine the REST API specifications and identify operations and attributes that are prone to mass assignment. We conducted a preliminary study on 100 APIs and found 25 prone to this vulnerability. We confirmed nine real vulnerable operations in six APIs.

WebAssembly has attracted great attention as a portable compilation target for programming languages. To facilitate in-depth studies about this technology, we have deployed Wasmizer, a tool that regularly mines GitHub projects and makes an up-to-date dataset of WebAssembly sources and their binaries publicly available. Presently, we have collected 2540 C and C++ projects that are highly-related to WebAssembly, and built a dataset of 8915 binaries that are linked to their source projects. To demonstrate an application of this dataset, we have investigated the presence of eight WebAssembly compilation smells in the wild.

Abstract Cloud computing provides a set of resources and services for customers on the Internet on demand and based on a pay as you go model. Cloud providers are looking to decrease costs and increase profits. Therefore, resource management and provisioning are very important for cloud providers. Automated scaling can be used to provide resources for user requests. Auto‐scaling can decrease the total operational costs for providers, although it does have its own cost and time overheads. In this paper, a new solution is presented for resource provisioning on multi‐layered cloud applications based on MAPE‐K loop. A weighted ensemble prediction model is proposed to estimate the resources utilization in each cloud layer. In addition, accuracy of the model and a regularization technique are used to regulate the weights of the models in the proposed hybrid prediction model. Furthermore, a decision tree‐based algorithm is presented to analyze status of the resources to make scaling decision. In addition, we propose a resource allocation algorithm that is based on Virtual Machine priority and request deadline in order to allocate requests on suitable resources. The experimental results indicate that the proposed algorithm has the best performance among its counterparts.

Summary Providing a pool of various resources and services to customers on the Internet in exchanging money has made cloud computing as one of the most popular technologies. Management of the provided resources and services at the lowest cost and maximum profit is a crucial issue for cloud providers. Thus, cloud providers proceed to auto‐scale the computing resources according to the users' requests in order to minimize the operational costs. Therefore, the required time and costs to scale‐up and down computing resources are considered as one of the major limits of scaling which has made this issue an important challenge in cloud computing. In this paper, a new approach is proposed based on MAPE‐K loop to auto‐scale the resources for multilayered cloud applications. K‐nearest neighbor (K‐NN) algorithm is used to analyze and label virtual machines and statistical methods are used to make scaling decision. In addition, a resource allocation algorithm is proposed to allocate requests on the resources. Results of the simulation revealed that the proposed approach results in operational costs reduction, as well as improving the resource utilization, response time, and profit.