João P. Vilela

Friendly Jamming for Wireless Secrecy

We analyze the role of jamming as a means to increase the security of wireless systems. Specifically, we characterize the impact of cooperative/friendly jamming on the secrecy outage probability of a quasi-static wiretap fading channel. We introduce jamming coverage and jamming efficiency as security metrics, and evaluate the performance of three different jamming strategies that rely on various levels of channel state information. The analysis provides insight for the design of optimal jamming configurations and indicates that one jammer is not enough to maximize both metrics simultaneously.

Position-Based Jamming for Enhanced Wireless Secrecy

Signal interference and packet collisions are typically viewed as negative factors that hinder wireless communication networks. When security is the primary concern, signal interference may actually be very helpful. Starting with a stochastic network model, we are able to show that packet collisions caused by jamming nodes can indeed be used effectively to attain new levels of secrecy in multiterminal wireless environments. To this effect, we propose a practical jamming protocol that uses the well-known request-to-send/clear-to-send (RTS/CTS) handshake of the IEEE 802.11 standard as a signaling scheme. Various jammer selection strategies are investigated depending on the position of source, destination, and jamming nodes. The goal is to cause as much interference as possible to eavesdroppers that are located in unknown positions, while limiting the interference observed by the legitimate receiver. To evaluate the performance of each strategy, we introduce and compute a measure for the secure throughput. Our results show that jamming can increase the levels of secrecy significantly albeit at a substantial cost in terms of energy efficiency.

An information-theoretic cryptanalysis of network coding - is protecting the code enough?

We consider the issue of confidentiality in multicast network coding, by assuming that the encoding matrices, based upon variants of random linear network coding, are given only to the source and sinks. Based on this assumption, we provide a characterization of the mutual information between the encoded data and the two elements that can lead to information disclosure: the matrices of random coefficients and, naturally, the original data itself. Our results, some of which hold even with finite block lengths, show that, predicated on optimal source-coding, information-theoretic security is achievable for any field size without loss in terms of decoding probability. It follows that protecting the encoding matrix is generally sufficient to ensure confidentiality of network coded data.

Collision-free jamming for enhanced wireless secrecy

We present a collision-free jammer selection policy for enhanced wireless secrecy. Jammers, selected from the neighbors of a source, are friendly in the sense that they are willing to help the source to transmit securely by causing interference/collisions to possible eavesdroppers. The proposed jammer selection policy results in the selection of the largest number of jammers that do not cause collisions among themselves. This enables jammers to assist the source to transmit securely by causing interference to eavesdroppers, while sending their own traffic into the network.

Predicting Traffic in the Cloud: A Statistical Approach

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 Drop box. Compared with other predictors, our solution exhibits the strongest correlation level and shows a close match with real observations.

Privacy-Preserving Data Mining: Methods, Metrics, and Applications

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.

Online Traffic Prediction in the Cloud: A Dynamic Window Approach

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.

Performance Analysis of Network Traffic Predictors in the Cloud

Predicting the inherent traffic behaviour of a network is an essential task, which can be used for various purposes, such as monitoring and managing the network’s infrastructure. However, the recent surge of dynamic environments, such as Internet of Things and Cloud Computing have hampered this task. This means that the traffic on these networks is even more complex, displaying a nonlinear behaviour with specific aperiodic characteristics during daily operation. Traditional network traffic predictors are usually based on large historical data bases which are used to train algorithms. This may not be suitable for these highly volatile environments, where the strength of the force exerted in the interaction between past and current values may change quickly with time. In light of this, a taxonomy for network traffic prediction models, including the review of state of the art, is presented here. In addition, an analysis mechanism, focused on providing a standardized approach for evaluating the best candidate predictor models for these environments, is proposed. These contributions favour the analysis of the efficacy and efficiency of network traffic prediction among several prediction models in terms of accuracy, historical dependency, running time and computational overhead. An evaluation of several prediction mechanisms is performed by assessing the Normalized Mean Square Error and Mean Absolute Percent Error of the values predicted by using traces taken from two real case studies in cloud computing.

Interleaved Concatenated Coding for Secrecy in the Finite Blocklength Regime

We propose a systematic concatenated coding scheme based on the combination of interleaving with powerful channel codes and jamming for wireless secrecy under the practical assumption of codes in the finite blocklength regime. The basic idea lies in generating a short random key that is used to shuffle/interleave information at the source, Alice. This key is then sent to the legitimate receiver, Bob, during a brief period of advantageous communication over the eavesdropper Eve (e.g., due to more interference from a jammer). Finally, the key is decoded at Bob to properly deinterleave the original information. Bob receives a better quality version of the interleaving key, therefore having the needed advantage over Eve. Information reliability is provided by a strong inner code, while security against Eve results from the proper selection of the outer code and interference levels over the key. We propose a methodology for selection of the outer code with reliability and security constraints. For that, we introduce bit error complementary cumulative distribution function metrics, suitable for security and reliability analysis of error correcting codes.

A characterization of uncoordinated frequency hopping for wireless secrecy

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.