Penetration testing model for mobile cloud computing applications / Ahmad Salah Mahmoud Al-Ahmad

Mobile cloud computing (MCC) technology possess features mitigating mobile limitations and enhancing cloud services. MCC application penetration testing issues are complex and unique which make the testing difficult for junior penetration testers. It is complex as MCC applications have three intersecting vulnerability domains, namely mobile, web, and cloud. The offloading process adds uniqueness and complexity to the MCC application penetration testing in terms of generating, selecting and executing test cases. To solve these issues, this thesis constructs a model for MCC application penetration testing that reduces the complexity, tackles the uniqueness and assists junior testers in conducting penetration tests on MCC applications more effectively and efficiently. The main objectives of this thesis are to discover the issues in conducting penetration testing on MCC applications and to construct and evaluate MCC application penetration testing model. Design science research methodology is applied with four phases: (i) Theoretical framework construction phase (ii) Model construction phase entails designing the components and processes of MCC application penetration to reduce the complexity and address offloading; (iii) Model implementation phase implements the components and processes of the model into model guidelines and integrated tool called PT2-MCC. This tool manages the repositories, generates and selects test cases, and implements the mobile agent component; (iv) Model evaluation phase applies case study approach and uses an evaluation framework to evaluate the model against selected testing quality and performance attributes. In model evaluation phase, a junior penetration tester conducted two case studies on two MCC applications built by extending two open source native mobile applications

A novel hybrid deep learning model for price prediction

Price prediction has become a major task due to the explosive increase in the number of investors. The price prediction task has various types such as shares, stocks, foreign exchange instruments, and cryptocurrency. The literature includes several models for price prediction that can be classified based on the utilized methods into three main classes, namely, deep learning, machine learning, and statistical. In this context, we proposed several models’ architectures for price prediction. Among them, we proposed a hybrid one that incorporates long short-term memory (LSTM) and Convolution neural network (CNN) architectures, we called it CNN-LSTM. The proposed CNN-LSTM model makes use of the characteristics of the convolution layers for extracting useful features embedded in the time series data and the ability of LSTM architecture to learn long-term dependencies. The proposed architectures are thoroughly evaluated and compared against state-of-the-art methods on three different types of financial product datasets for stocks, foreign exchange instruments, and cryptocurrency. The obtained results show that the proposed CNN-LSTM has the best performance on average for the utilized evaluation metrics. Moreover, the proposed deep learning models were dominant in comparison to the state-of-the-art methods, machine learning models, and statistical models

A Semantic Approach for Outlier Detection in Big Data Streams

In recent years, the world faced a big revolution in data generation and collection technologies. The volume, velocity and veracity of data have changed drastically and led to new types of challenges related to data analysis, modeling and prediction. One of the key challenges is related to the semantic analysis of textual data especially in big data streams settings. The existing solutions focus on either topic analysis or the sentiment analysis. Moreover, the semantic outlier detection over data streams as one of the key problems in data mining and data analysis fields has less focus. In this paper, we introduce a new concept of semantic outlier through which the topic of the textual data is considered as the primary content of the data stream while the sentiment is considered as the context in which the data has been generated and affected. Also, we propose a framework for semantic outlier detection in big data streams which incorporates the contextual detection concepts. The advantage of the proposed concept is that it incorporates both topic and sentiment analysis into one single process; while at the same time the framework enables the implementation of different algorithms and approaches for semantic analysis

The Impact of Organizational Development on Maximizing Business Intelligence in Jordanian Joint Stock Companies

The study aimed to measure the impact of organizational development on maximizing business intelligence in Jordanian industrial joint stock companies. The study was based on analytical descriptive methodology. The study community is from Orange Jordan as a case study.The sampling unit consisted of managers working in the senior and middle management of industrial companies in Jordan. A total of 264 questionnaires were collected and retrieved (258) of which were (97.7%), all of which were valid for analysis. The survey sample was composed of general managers, their deputies, assistants and department heads according to the study sample.The study concluded with a number of results, the most important of which was the existence of a statistically significant effect at the level of α (= 0.05) for the organizational development in its dimensions (development, empowerment, recruitment, innovation, organization) to maximize business intelligence (data collection, Data, reporting, information transfer) in Jordanian industrial joint stock companies. The study recommended a number of recommendations, the most important of which was the need to raise the awareness of the Jordanian industrial companies' management about the importance of organizational development as one of the fundamental concepts in modern management. It also recommended increasing the interest of administrative leaders in studying and understanding the characteristics of effective business intelligence dimensions and methods. DOI: 10.7176/EJBM/11-14-11 Publication date:May 31st 201

Fuzzy Set-Based Contingency Estimating And Management

Contingency estimating and management are critical and necessary functions for successful delivery of construction projects. Considering such importance, academics and industry professionals proposed a wide range of methods for risk quantification and accordingly for contingency estimating. Considerably less work was directed to contingency management including its depletion to mitigate risk over project durations. Generally, there are two types of risks; 1) known risks which can be identified, evaluated, planned and budgeted for and 2) unknown risks which may occurred. These two categories of risks required a cost and time contingency, even if they weren’t planned for, in order to mitigate their impact in an orderly manner. In this respect, the importance of contingency management become critical in view of increasing project complexity and difficulty of estimating and/or allocating sufficient contingencies to mitigate risks encountered during project execution. This thesis focuses on the contingency management from two perspectives; estimating and depletion of contingency over project durations. A new method is developed using fuzzy sets theory, along with a set of measures, indices, and ratios to model the uncertainty inherent in this process and estimate cost contingencies. The uncertainties are expressed in the developed model using a set of measures and indicators including possibility measure, agreement index, fussiness measure, ambiguity measure, quality fuzzy number index, fuzziness expected value ratio, and ambiguity expected value ratio. These measures, indices, and ratios provide not only the possibility of having adequate contingency but also address issues of precision and vagueness associated with the uncertainty involved in a generic computational platform. The thesis, also, presents a comparison between fuzzy existing methods, Monte Carlo Simulation, PERT, and a proposed direct fuzzy set-based method. As to depletion, the thesis presents a management procedure focusing on depletion of the contingency. The developed procedure makes use of policies and procedures followed by leading construction organizations and owners of major constructed facilities. The developed method and its computational platform were coded using VB.net Programming. Two project examples drawn from the literature are analysed to demonstrate the use of developed method and to illustrate its capabilities beyond those of traditional Methods

Fuzzy Set-based Risk Management for Construction Projects

Efficient and comprehensive risk management is critical for successful delivery of engineering, procurement, and construction management (EPCM) projects. Complexity of construction projects is on the rise, which makes it necessary to model uncertainties and to manage risk items related to this class of projects. For decades, researchers and construction practitioners worked together to introduce methods for risk identification and assessment. Considerably less effort was directed towards the development of methods for mitigation, monitoring, and control. The respective individual limitations of these methods prevent the development of comprehensive model which satisfies the needs of practitioners. In this research a comprehensive risk management model “CRMM” is developed to address the limitations of existing methods and to fill the gap between research and practice. The developed model implements a micro system approach to introduce a novel risk identification methodology that provides a systematic procedure to identify risk associated with construction projects. The identification procedure implements root cause analysis and brainstorming technique to identify risk items, consequences, and root causes. The developed CRMM also introduces new method for determination of risk ownership utilizing fuzzy set theory and “One Risk – One Owner” concept. The ownership determination method allocates risk to the owner with highest ability, effectiveness, and capacity to deal with that risk. It also introduces a new qualitative and quantitative evaluation process that utilizes fuzzy set theory and fuzzy probability theory, as well as a new risk mapping procedure which allows for the determination of risk level associated with any project component (e.g., category). The quantitative assessment methodology allows for the use of linguistic and numeric fuzzy evaluations. Fuzzy Linguistic-Numeric Conversion Scheme (FLNCS) is introduced to convert the linguistic evaluations into numeric. The quantitative assessment methodology also introduces the pre-mitigation contingency that represents the contingency fund required for a risk in case no mitigation strategy is implemented. In this respect a novel risk mitigation framework is developed to generate and evaluate possible mitigation strategies for each risk being considered. It also provides a selection procedure which allows users to select the most effective mitigation strategy; making use of fuzzy set theory. The mitigation methodology introduces the post-mitigation contingency that quantifies the contingency required for the selected mitigation strategy. Performance of selected mitigation strategy is monitored using a newly developed risk monitoring method that compares the actually depleted contingency to the post mitigation contingency. The developed monitoring method provides an early warning that alerts users of detected possible failure of selected mitigation strategy. It also determines the correct time for initiation of control process based on a set of qualitative factors. Once risk control process is initiated, the developed control method identifies, evaluates, and selects the most effective control action(s) to support the selected mitigation strategy. In cases where the selected control action fails, the developed control method notifies the user to revise the risk management plan. These notifications allows user to avoid potential failures of similar risk items which are expected to occur in the future. The developed CRMM was coded using VB.Net under Microsoft® windows and .NET framework environment to facilitate its application. A set of case studies are collected from literature and analysed to validate the developed methods within CRMM and to illustrate their essential features. Also, a numerical example elucidates the complete computational processes of the developed comprehensive model

EFFECT OF ROLE CONFLICT AND ROLE AMBIGUITY ON EMPLOYEE CREATIVITY

The objective of the present study was to investigate the effects of role conflict and role ambiguity on employee’s creativity. Exactly how role stress and various performances of individuals are related has received considerable attention, in which stress has been found to affect individual creativity. However, exactly how role stress (role conflict and role ambiguity) and employee creativity are related has seldom been examined empirically. A sample size of 100 was selected and standard questionnaires were distributes among the employees of three public sector universities in Peshawar KPK Pakistan. The conclusions drawn from the study were that role conflict and role ambiguity have negative relationship with employees creativity. Implications of the findings of this study and possible directions for future research are also discussed

Bayesian Markov switching model for BRICS currencies' exchange rates

Exchange rate modeling has always fascinated researchers because of its complex macroeconomic dynamics. This study documents the exchange rate dynamics of major emerging economies after accounting for their macroeconomic cycles and explores the Bayesian Vector Error Correction Model (VECM) Markov Regime switching model, which uses time-varying transition probabilities. The main objective is to study the exchange rate dynamics of Brazil, Russia, India, China, and South Africa (BRICS) vis-à-vis the US dollar. The Bayesian setup uses two hierarchal shrinkage priors, the normal-gamma (NG) prior and the Litterman prior, for parameters' estimation. These shrinkage priors allow for a more comprehensive assessment of the regime-specific coefficients. The model performed well in differentiating between the two regimes for all currencies. The Russian ruble was identified to be the most depreciated currency, whereas the African Rand was the most appreciated. The evaluation of model features revealed that many regime-specific coefficients differed significantly from their common mean. A forecasting exercise was then performed for the out-of-sample period to assess the model's performance. A significant improvement was observed over the basic random walk (RW) model and the linear Bayesian vector autoregression (BVAR) model

A Laboratory Based Study of Hydraulic Simulation of Leakage in Water Distribution Networks

It is obvious to all people the importance of water as an essential element for life, hence, water loss is a life-threatening and alarming predictor of the future. Leakage problem is one of the most important causes of water loss in water systems; therefore, it was and is still a matter of attention of many researchers, who are in search of the most effective methods to solve this problem using many techniques. These techniques vary with one another in terms of accuracy, cost and speed of obtaining results. This research paper presents a part of an extensive research work, which aims to develop a geospatial approach for solving the leakage detection problem in water systems using an integrated geospatial system. This paper will show a sample of the results that has been obtained through a lab experiment, which explains the changes in hydraulic behavior of the network due to the change in leakage size and leakage location as a step for validating the mentioned approach. Keywords: Leakage detection, water distribution networks, GIS, Hydraulic modeling