Future Computing and Informatics Journal最新文献

HTTP Get Flood attack is known as the most common DDOS attack on the application layer with a frequency of 21 percent in all attacks. Since a huge amount of requests is sent to the Web Server for receiving pages and also the volume of responses issued by the server is much more than the volume received by zombies in this kind of attack, hence it could be done by small botnets; in the other hand, because every zombie attempts to issue the request by the use of its real address, carries out all stages of the three-stage handshakes, and the context of the requests is fully consistent with the HTTP protocol, the techniques of fake address detection and anomaly detection in text could not be employed. The mechanisms that are used to deal with this attack not only have much processing overload but also may cause two kinds of “False Negative” (To realize wrongly the fake traffic as the real traffic) and “False Positive” (To realize wrongly the real traffic as the fake traffic) errors. Therefore a method is proposed that is able to adapt itself to the traffic by the use of low processing overload and it has less error than the similar systems and using this way.

Software development industry has witnessed the growth of the agile movement and approaches. Applying the agile approaches and practices in the distributed environment will lead to gain a lot of benefits such as reduced costs, higher efficiency and better customization, on the other hand it will face many challenges for example working in different time zones, requirements changes, personal selection and knowledge management. In order to gain these benefits, it should address the challenges that will face the agile approaches in a distributed environment. One of the main challenges is managing the requirements changes during the process of distributed agile software development. Only few researches published in the literature, addressed the problem of requirements changes during the development process in distributed agile development. Most of the published researches in this context are based on industrial experiences which increases the need for combining the industry with academia within this area. In this paper an approach to manage requirements changes in distributed agile development is introduced. This suggested approach works to fill the gap between the industry and research in distributed agile development by combining the industrial practice and academic technique. This approach is based on a proposed feature model called a features tree. The approach is associated with a supporting software tool that helps to manage the requirement changes in distributed agile development. The supporting tool is tested and evaluated in real environments by software development professionals using an exhaustive set of criteria, and the results are promising.

The paper focusses on the implementation of cooperative Spider Monkey Optimization Algorithm (SMO) to design and optimize the fuzzy rule base. Spider Monkey Optimization Algorithm is a fission-fusion based Swarm Intelligence algorithm. Cooperative Spider Monkey Algorithm is an off-line algorithm used to optimize all the free parameters in a fuzzy rule base. The Spider Monkeys are divided into various groups the solution from each group represents a fuzzy rule. These groups work in a cooperative way to design the whole fuzzy rule base. Simulation on fuzzy rules of two nonlinear controllers is done with a parametric study to verify the performance of the algorithm. It is observed that the root mean square error (RMSE) is least in the case of SMO than the other evolutionary algorithms applied in the literature to solve the problem of fuzzy rule designs like Particle Swarm Optimization (PSO), Ant Colony Optimization algorithm (ACO) algorithms.

Nowadays, there is an increasing demand in mining interesting patterns from the big data. The process of analyzing such a huge amount of data is really computationally complex task when using traditional methods. The overall purpose of this paper is in twofold. First, this paper presents a novel approach to identify consistent and inconsistent association rules from sales data located in distributed environment. Secondly, the paper also overcomes the main memory bottleneck and computing time overhead of single computing system by applying computations to multi node cluster. The proposed method initially extracts frequent itemsets for each zone using existing distributed frequent pattern mining algorithms. The paper also compares the time efficiency of Mapreduce based frequent pattern mining algorithm with Count Distribution Algorithm (CDA) and Fast Distributed Mining (FDM) algorithms. The association generated from frequent itemsets are too large that it becomes complex to analyze it. Thus, Mapreduce based consistent and inconsistent rule detection (MR-CIRD) algorithm is proposed to detect the consistent and inconsistent rules from big data and provide useful and actionable knowledge to the domain experts. These pruned interesting rules also give useful knowledge for better marketing strategy as well. The extracted consistent and inconsistent rules are evaluated and compared based on different interestingness measures presented together with experimental results that lead to the final conclusions.

Nowadays the software is becoming complex as clients expect a number of functionalities in software. In such scenario, information leakage can't be avoided. As a result, a lot of research is going on to develop tools, methods and policies to find and minimize the leakage. The paper proposes a method to provide a measure, especially to the IT organizations to find how the information leakage at one portion of the software can propagate leakage risk to the other portions of the software or entire software. The paper uses the quantitative analysis of information leakage and cost function based statistical method to find the leakage risk propagation in the software. The method proposed in the paper facilitates the organizations by allowing them to set the organization specific parameters. The proposed method has been applied to the function of Linux to demonstrate the information leakage risk propagation. When organizations find information leakage in the software, their sustaining engineering or quality management teams simply rectify the software portion. But it becomes difficult for the organizations to document the overall mitigation of the risk of leakage. Thus, using the proposed method, organizations will be able to quantify the information leakage risk mitigation.

Power consumption is a major factor to be taken into consideration when using mobile devices in the IoT field. Good Power management requires proper understanding of the way in which it is being consumed by the end-devices. This paper is a continuation of the work in Ref. [1] and proposes an energy saving API for the Android Operating System in order to help developers turn their applications into energy-aware ones. The main features heavily used for building smart applications, greatly impact battery life of Android devices and which have been taken into consideration are: Screen brightness, Colour scheme, CPU frequency, 2G/3G network, Maps, Low power localisation, Bluetooth and Wi-Fi. The assessment of the power-saving API has been performed on real Android devices and also compared to the most powerful power-saving applications – DU Battery Saver and Battery Saver 2016 – currently available on the Android market. Comparisons demonstrate that the Enersave API has a significant impact on power saving when incorporated in android applications. While DU Battery Saver and Battery Saver 2016 help saving 22.2% and 40.5% of the battery power respectively, the incorporation of the Enersave API in android applications can help save 84.6% of battery power.

When forecasting time series, it is important to classify them according linearity behavior that the linear time series remains at the forefront of academic and applied research, it has often been found that simple linear time series models usually leave certain aspects of economic and financial data unexplained. The dynamic behavior of most of the time series in our real life with its autoregressive and inherited moving average terms issue the challenge to forecast nonlinear times series that contain inherited moving average terms using computational intelligence methodologies such as neural networks. It is rare to find studies that concentrate on forecasting nonlinear times series that contain moving average terms. In this study, we demonstrate that the common neural networks are not efficient for recognizing the behavior of nonlinear or dynamic time series which has moving average terms and hence low forecasting capability. This leads to the importance of formulating new models of neural networks such as Deep Learning neural networks with or without hybrid methodologies such as Fuzzy Logic.

Key Performance Indicators (KPIs) allows gathering knowledge and exploring the best way to achieve organization goals. Many researchers have provided different ideas for determining KPI's either manually, and semi-automatic, or automatic which is applied in different fields. This work concentrates on providing a survey of different approaches for exploring and predicting key performance indicators (KPIs).

Most existing databases suffer from data inconsistencies. Enhancing data quality efforts are necessary to resolve this issue. In this paper, two techniques are proposed for mining accurate conditional functional dependencies rules from such databases to be employed for data cleaning. The idea of the proposed techniques is to mine firstly maximal closed frequent patterns, then mine the dependable conditional functional dependencies rules with the help of lift measure. Moreover, data repairing algorithm is proposed for fixing inconsistent tuples found in the database exploiting the generated rules. An extensive experimental is conducted study to confirm the effectiveness of the proposed techniques compared with existing technique on both real-life and synthetic medical data sets.

Cloud computing is becoming increasingly popular. Information technology market leaders, e.g., Microsoft, Google, and Amazon, are extensively shifting toward cloud-based solutions. However, there is isolation in the cloud implementations provided by the cloud vendors. Limited interoperability can cause one user to adhere to a single cloud provider; thus, a required migration of an application or data from one cloud provider to another may necessitate a significant effort and/or full-cycle redevelopment to fit the new provider's standards and implementation. The ability to move from one cloud vendor to another would be a step toward advancing cloud computing interoperability and increasing customer trust. This study proposes a cloud broker solution to fill the interoperability gap between different software-as-a-service providers. The proposed cloud broker was implemented and tested on a real enterprise application dataset. The migration process was completed and it worked correctly, according to a specified mapping model.