The proliferation of inter-connected devices in critical industries, such as healthcare and power grid, is changing the perception of what constitutes critical infrastructure. The rising interconnectedness of new critical industries is driven by the growing demand for seamless access to information as the world becomes more mobile and connected and as the Internet of Things (IoT) grows. Critical industries are essential to the foundation of today’s society, and interruption of service in any of these sectors can reverberate through other sectors and even around the globe. In today’s hyper-connected world, the critical infrastructure is more vulnerable than ever to cyber threats, whether state sponsored, criminal groups or individuals. As the number of interconnected devices increases, the number of potential access points for hackers to disrupt critical infrastructure grows. This new attack surface emerges from fundamental changes in the critical infrastructure of organizations technology systems. This paper aims to improve understanding the challenges to secure future digital infrastructure while it is still evolving. After introducing the infrastructure generating big data, the functionality-based fog architecture is defined. In addition, a comprehensive review of security requirements in fog-enabled IoT systems is presented. Then, an in-depth analysis of the fog computing security challenges and big data privacy and trust concerns in relation to fog-enabled IoT are given. We also discuss blockchain as a key enabler to address many security related issues in IoT and consider closely the complementary interrelationships between blockchain and fog computing. In this context, this work formalizes the task of securing big data and its scope, provides a taxonomy to categories threats to fog-based IoT systems, presents a comprehensive comparison of state-of-the-art contributions in the field according to their security service and recommends promising research directions for future investigations.

With the terrific growth of digital data and associated technologies, there is an emerging trend, where industries become rapidly digitized. These technologies are providing great opportunities to identify and resolve different problems. In particular, the telecommunication industry is facing a serious problem of customer churn relating to, the customers who are going to abandon their established relation with the business/network in the near future. This problem cannot only affect the rapid growth of the business but can also affect the revenues. Therefore, many customer churn prediction (CCP) models have been introduced but not yielding the desired performance in CCP. This is because there can be many factors, that contribute to customer churn which are still unexplored. In this paper, we focus on determining the effectiveness of the factors, i.e. lower and upper distance between the samples, are considered by the proposed model for the CCP. Further, we demonstrate a novel solution pertaining to the telecommunication sector showing the hidden factors considered for predicting the customer churn. Finally, we investigate the effects of both types of samples: those samples that are low distance and the upper distance (in terms of relevance) to the majority samples in given publicly available dataset. As a result of the study, we found that lower distance test set (LDT) samples have obtained best performance as compare to upper distance test set (UDT) samples in term of increased in the accuracy, f-measures, precision and recall when the uncertain sample size increases. Because the classification performance on upper distance samples remain almost the same when the size of samples increased in the test set.

Different types of noise from the surrounding always interfere with speech and produce annoying signals for the human auditory system. To exchange speech information in a noisy environment, speech quality and intelligibility must be maintained, which is a challenging task. In most speech enhancement algorithms, the speech signal is characterized by Gaussian or super-Gaussian models, and noise is characterized by a Gaussian prior. However, these assumptions do not always hold in real-life situations, thereby negatively affecting the estimation, and eventually, the performance of the enhancement algorithm. Accordingly, this paper focuses on deriving an optimum low-distortion estimator with models that fit well with speech and noise data signals. This estimator provides minimum levels of speech distortion and residual noise with additional improvements in speech perceptual aspects via four key steps. First, a recent transform based on an orthogonal polynomial is used to transform the observation signal into a transform domain. Second, the noise classification based on feature extraction is adopted to find accurate and mutable models for noise signals. Third, two stages of nonlinear and linear estimators based on the minimum mean square error (MMSE) and new models for speech and noise are derived to estimate a clean speech signal. Finally, the estimated speech signal in the time domain is determined by considering the inverse of the orthogonal transform. The results show that the average classification accuracy of the proposed approach is 99.43%. In addition, the proposed algorithm significantly outperforms existing speech estimators in terms of quality and intelligibility measures.

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