Biblio

The paper provides an analysis of the performance of an administrative component that helps the hypervisor to manage the resources of guest operating systems under fluctuation workload. The additional administrative component provides an extra layer of security to the guest operating systems and system as a whole. In this study, an administrative component was implemented by using Xen-hypervisor based para-virtualization technique and assigned some additional roles and responsibilities that reduce hypervisor workload. The study measured the resource utilizations of an administrative component when excessive input/output load passes passing through the system. Performance was measured in terms of bandwidth and CPU utilisation Based on the analysis of administrative component performance recommendations have been provided with the goal to improve system availability. Recommendations included detection of the performance saturation point that indicates the necessity to start load balancing procedures for the administrative component in the virtualized environment.

For a long time, the most important approach for detecting malicious applications was the use of static, hash-based signatures. This approach provides a fast response time, has a low performance overhead and is very stable due to its simplicity. However, with the rapid growth in the number of malware, as well as their increased complexity in terms of polymorphism and evasion, the era of reactive security solutions started to fade in favor of new, proactive approaches such as behavior based detection. We propose a novel approach that uses an interpreter virtual machine to run proactive behavior heuristics from bytecode signatures, thus combining the advantages of behavior based detection with those of signatures. Based on our approximation, using this approach we succeeded to reduce by 85% the time required to update a behavior based detection solution to detect new threats, while continuing to benefit from the versatility of behavior heuristics.

Internet of Things (IoT) stack models differ in their architecture, applications and needs. Hence, there are different approaches to apply IoT; for instance, it can be based on traditional data center or based on cloud computing. In fact, Cloud-based IoT is gaining more popularity due to its high scalability and cost effectiveness; hence, it is becoming the norm. However, Cloud is usually located far from the IoT devices and some recent research suggests using Fog-Based IoT by using a nearby light-weight middleware to bridge the gap and to provide the essential support and communication between devices, sensors, receptors and the servers. Therefore, Fog reduces centrality and provides local processing for faster analysis, especially for the time-sensitive applications. Thus, processing is done faster, giving the system flexibility for faster response time. Fog-Based Internet of Things security architecture should be suitable to the environment and provide the necessary measures to improve all security aspects with respect to the available resources and within performance constraints. In this work, we discuss some of these challenges, analyze performance of Fog based IoT and propose a security scheme based on MQTT protocol. Moreover, we present a discussion on security-performance tradeoffs.

Using shape context descriptors in the distance uneven grouping and its more extensive description of the shape feature, so this descriptor has the target contour point set deformation invariance. However, the twisted adhesions verification code have more outliers and more serious noise, the above-mentioned invariance of the shape context will become very bad, in order to solve the above descriptors' limitations, this article raise a new algorithm based on the relative shape context and point pattern matching to identify codes. And also experimented on the CSDN site's verification code, the result is that the recognition rate is higher than the traditional shape context and the response time is shorter.

Within few years, Cloud computing has emerged as the most promising IT business model. Thanks to its various technical and financial advantages, Cloud computing continues to convince every day new users coming from scientific and industrial sectors. To satisfy the various users' requirements, Cloud providers must maximize the performance of their IT resources to ensure the best service at the lowest cost. The performance optimization efforts in the Cloud can be achieved at different levels and aspects. In the present paper, we propose to introduce a fuzzy logic process in scheduling strategy for public Cloud in order to improve the response time, processing time and total cost. In fact, fuzzy logic has proven his ability to solve the problem of optimization in several fields such as data mining, image processing, networking and much more.

Recently, there has been a pronounced increase of interest in the field of renewable energy. In this area power inverters are crucial building blocks in a segment of energy converters, since they change direct current (DC) to alternating current (AC). Grid connected power inverters should operate in synchronism with the grid voltage. In this paper, the structure of a power system based on adaptive filtering is described. The main purpose of the adaptive filter is to adapt the output signal of the inverter to the corresponding load and/or grid signal. By involving adaptive filtering the response time decreases and quality of power delivery to the load or grid increases. A comparative analysis which relates to power system operation without and with adaptive filtering is given. In addition, the impact of variable impedance of load on quality of delivered power is considered. Results which relates to total harmonic distortion (THD) factor are obtained by Matlab/Simulink software.