Abstract
Traffic prediction is a fundamental tool that captures the inherent behavior of a network and can be used for monitoring and managing network traffic. Online traffic prediction is usually performed based on large historical data used in training algorithms. This may not be suitable to highly volatile environments, such as cloud computing, where the coupling between observations decreases quickly with time. We propose a dynamic window size approach for traffic prediction that can be incorporated with different traffic predictions mechanisms, making them suitable to online traffic prediction by adapting the amount of traffic that must be analyzed in accordance to the variability of data traffic. The evaluation of the proposed solution is performed for several prediction mechanisms by assessing the Normalized Mean Square Error and Mean Absolute Percent Error of predicted values over observed values from a real cloud computing data set, collected by monitoring the utilization of Dropbox.

Abstract
Network traffic prediction is a fundamental tool to harness several management tasks, such as monitoring and managing network traffic. Online traffic prediction is usually performed based on large sets of historical data used in training algorithms, for example, to determine the size of static windows to bound the amount of traffic under consideration. However, using large sets of historical data may not be suitable in highly volatile environments, such as cloud computing, where the coupling between time series observations decreases rapidly with time. To fill this gap, this work presents a dynamic window size algorithm for traffic prediction that contains a methodology to optimize a threshold parameter alpha that affects both the prediction and computational cost of our scheme. The alpha parameter defines the minimum data traffic variability needed to justify dynamic window size changes. Thus, with the optimization of this parameter, the number of operations of the dynamic window size algorithm decreases significantly. We evaluate the alpha estimation methodology against several prediction models by assessing the normalized mean square error and mean absolute percent error of predicted values over observed values from two real cloud computing datasets, collected by monitoring the utilization of Dropbox, and a data center dataset including traffic from several common cloud computing services. Copyright (C) 2016 John Wiley & Sons, Ltd.

Abstract
The collection and analysis of data are continuously growing due to the pervasiveness of computing devices. The analysis of such information is fostering businesses and contributing beneficially to the society in many different fields. However, this storage and flow of possibly sensitive data poses serious privacy concerns. Methods that allow the knowledge extraction from data, while preserving privacy, are known as privacy-preserving data mining (PPDM) techniques. This paper surveys the most relevant PPDM techniques from the literature and the metrics used to evaluate such techniques and presents typical applications of PPDM methods in relevant fields. Furthermore, the current challenges and open issues in PPDM are discussed. © 2017 IEEE.

Abstract
Monitoring and managing traffic are vital elements to the operation of a network. Traffic prediction is an essential tool that captures the underlying behavior of a network and can be used, for example, to detect anomalies by defining acceptable data traffic thresholds. In this context, most current solutions are heavily based on historical time data, which makes it difficult to employ them in a dynamic environment such as cloud computing. We propose a traffic prediction approach based on a statistical model where observations are weighted with a Poisson distribution inside a sliding window. The evaluation of the proposed method is performed by assessing the Normalized Mean Square Error of predicted values over observed values from a real cloud computing dataset, collected by monitoring the utilization of Dropbox. Compared with other predictors, our solution exhibits the strongest correlation level and shows a close match with real observations.

Abstract
We characterize the secrecy level of communication under Uncoordinated Frequency Hopping, a spread spectrum scheme where a transmitter and a receiver randomly hop through a set of frequencies with the goal of deceiving an adversary. In our work, the goal of the legitimate parties is to land on a given frequency without the adversary eavesdroppers doing so, therefore being able to communicate securely in that period, that may be used for secret-key exchange. We also consider the effect on secrecy of the availability of friendly jammers that can be used to obstruct eavesdroppers by causing them interference. Our results show that tuning the number of frequencies and adding friendly jammers are effective countermeasures against eavesdroppers.

Abstract
We propose a coding scheme based on the combination of interleaving with systematic channel codes for secrecy. The basic idea consists of generating a random interleaving key that is used to shuffle/interleave information at the source. The message and the interleaving key are then both encoded with a systematic code and the part related to the interleaving key is removed/punctured before being sent to the channel, hence operating as a hidden key for any receiver (legitimate or not) that needs to deinterleave the message. Successfully obtaining the original message then depends on determining the interleaving key, which can only be done through the parity bits that result from jointly encoding the interleaving key and the message. We provide a method to determine the necessary signal-to-noise ratio difference that enables successful reception at the legitimate receiver without the eavesdropper having access to the message. In addition, we provide evidence that this scheme may also be used to turn a realistic channel into a discrete memoryless channel, thus providing a first practical implementation of an abstract channel that can be employed with a wiretap code to provide information-theoretic security guarantees.