No abstract

Cloud computing is a technology that allows the end-users to access the network through a shared area of resources. As the demand for the cloud computing increases, vulnerabilities in the service provision also increase. EDoS is one of the attacks that take over the provider, financially affecting the various organizations which use the cloud data. This paper utilizes fuzzy entropy and lion neural learner (FLNL) for the classification of cloud users to mitigate EDoS attacks in the cloud. This technique includes a training phase, which creates a log file using various parameters and then transforms the features into database considering certain key features. There are two important stages in this classification approach: feature selection and classification. Here, the fuzzy entropy function is utilized for feature selection which effectively selects useful features without information loss. The classification is performed using lion neural learner (LNL) which incorporates Lion algorithm (LA) into the neural network and uses Levenberg–Marquardt (LM) algorithm. The experimental results finalize that the proposed FLNL is effective with 89% precision, 78% recall, and 83.13% of f-measure compared with the existing Naïve Bayes (NB), Neural [Formula: see text] Propagation [Formula: see text], and Neural [Formula: see text]–Marquardt [Formula: see text].

Using Eucalyptus Systems' private cloud solution, an institute can build Virtual Computing Lab (VCL) that can satisfy the requirements of an institute, but it is assumed that infinite computing resources are available on demand thereby eliminating the need for cloud computing users to plan far ahead for provisioning.. However, due to extensive usage of computational resources, cluster controller (CC) component of Eucalyptus becomes a bottleneck, hampering performance of cloud computing environment. To overcome the drawbacks of Eucalyptus, diffused cloud approach is proposed based on master-slave concept where one master and multiple slaves serve the resources to the clients. This approach improves the performance of the server and would allow cloud servers to extend their computational power by dynamic resource discovery over the network. This architecture allows new clients to request virtual machines, and the server makes the choice of running the requested virtual machine either on previously available slaves, or on the clients who are recently registered into a set of slaves. Thus this architecture reduces the probability of occurrence of network bottlenecks and ensures that sufficient resources are always available to the end users, thus implementing the concept "Cloud never Dies". In order to demonstrate the performance of this novel architecture we provide and interpret several experimental results.

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Smart devices are exciting and upcoming advancements in the field of Internet of things (IOT). It is an application of Raspberry Pi, from furniture in the residential and commercial sector to everyday devices like watches and headphones, every object is becoming smarter and hence, making human lives much easier and efficient. Users can actively interact with these devices using voice commands. These smart devices can also assist a user in their personal daily activities as well as in medical use. Currently, various variations of the smart mirror based on software and hardware compatibility as well as based on applications and uses are available in the market. The goal of this paper is to understand the working, various applications, and purposes of the smart mirror according to the varying features in different versions. This paper critically analyses the different versions of the smart mirror and its different features and applications.