P. Salvo Rossi

Classification of Network Traffic via Packet-Level Hidden Markov Models

Traffic classification and identification is a fertile research area. Beyond Quality of Service, service differentiation, and billing, one of the most important applications of traffic classification is in the field of network security. This paper proposes a packet-level traffic classification approach based on Hidden Markov Model (HMM). Classification is performed by using real network traffic and estimating - in a combined fashion - Packet Size (PS) and Inter Packet Time (IPT) characteristics, thus remaining applicable to encrypted traffic too. The effectiveness of the proposed approach is evaluated by considering several traffic typologies: we applied our model to real traffic traces of Age of Mythology and Counter Strike (two Multi Player Network Games), HTTP, SMTP, Edonkey, PPlive (a peer-to-peer IPTV application), and MSN Messenger. An analytical basis and the mathematical details regarding the model are given. Results show how the proposed approach is able to classify network traffic by using packet-level statistical properties and therefore it is a good candidate as a component for a multi-classification framework.

Slepian-Based Two-Dimensional Estimation of Time-Frequency Variant MIMO-OFDM Channels

A linear channel estimator for multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems, based on a two-dimensional Slepian expansion, is presented. The estimator is meant to be part of an iterative receiver. We consider both estimation based on pilots only and on pilots and data, the latter considered as a reference for the case when feedback from decoders is exploited. Performances are analyzed via computer simulations comparing the relative minimum square error (RMMSE) of an analogous one-dimensional estimator and the proposed extension.

Joint Twofold-Iterative Channel Estimation and Multiuser Detection for MIMO-OFDM Systems

This paper presents an iterative receiver for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems over time-variant wireless channels. The receiver performs joint decoding, channel estimation, and multiuser detection, with soft information iteratively provided by the single-user decoders. Time-variance is effectively taken into account exploiting the properties of the discrete prolate spheroidal (DPS) sequences, being the bandlimited sequences with maximum energy concentration in time. Turbo codes are used for each transmit antenna, thus the receiver presents an iterative structure also in the single-user case. Simulation results for the performance are presented in terms of bit error rate (BER) and normalized mean square error (NMSE) vs signal-to-noise ratio (SNR). The effects of the number of external and internal iterations as well as the number of pilots on the performance of the system are investigated.

Joint end-to-end loss-delay hidden Markov model for periodic UDP traffic over the Internet

Performance of real-time applications on network communication channels is strongly related to losses and temporal delays. Several studies showed that these network features may be correlated and exhibit a certain degree of memory such as bursty losses and delays. The memory and the statistical dependence between losses and temporal delays suggest that the channel may be well modeled by a hidden Markov model (HMM) with appropriate hidden variables that capture the current state of the network. In this paper, an HMM is proposed that shows excellent performance in modeling typical channel behaviors in a set of real packet links. The system is trained with a modified version of the Expectation-Maximization (EM) algorithm. Hidden-state analysis shows how the state variables characterize channel dynamics. State-sequence estimation is obtained by the use of Viterbi algorithm. Real-time modeling of the channel is the first step to implement adaptive communication strategies.

Optimality of Received Energy in Decision Fusion Over Rayleigh Fading Diversity MAC With Non-Identical Sensors

Received-energy test for non-coherent decision fusion over a Rayleigh fading multiple access channel (MAC) without diversity was recently shown to be optimum in the case of conditionally mutually independent and identically distributed (i.i.d.) sensor decisions under specific conditions [C. R. Berger, M. Guerriero, S. Zhou, and P. Willett, “PAC vs. MAC for Decentralized Detection Using Noncoherent Modulation,” IEEE Trans. Signal Process., vol. 57, no. 9, pp. 3562-2575, Sep. 2009], [F. Li, J. S. Evans, and S. Dey, “Decision Fusion Over Noncoherent Fading Multiaccess Channels,” IEEE Trans. Signal Process., vol. 59, no. 9, pp. 4367-4380, Sep. 2011]. Here, we provide a twofold generalization, allowing sensors to be non identical on one hand and introducing diversity on the other hand. Along with the derivation, we provide also a general tool to verify optimality of the received energy test in scenarios with correlated sensor decisions. Finally, we derive an analytical expression of the effect of the diversity on the large-system performances, under both individual and total power constraints.

One-Bit Decentralized Detection With a Rao Test for Multisensor Fusion

In this letter, we propose the Rao test as a simpler alternative to the generalized likelihood ratio test (GLRT) for multisensor fusion. We consider sensors observing an unknown deterministic parameter with symmetric and unimodal noise. A decision fusion center (DFC) receives quantized sensor observations through error-prone binary symmetric channels and makes a global decision. We analyze the optimal quantizer thresholds and we study the performance of the Rao test in comparison to the GLRT. Also, a theoretical comparison is made and asymptotic performance is derived in a scenario with homogeneous sensors. All the results are confirmed through simulations.

A Systematic Framework for Composite Hypothesis Testing of Independent Bernoulli Trials

This letter is focused on the classic problem of testing samples drawn from independent Bernoulli probability mass functions, when the success probability under the alternative hypothesis is not known. The goal is to provide a systematic taxonomy of the viable detectors (designed according to theoretically-founded criteria) which can be used for the specific instance of the problem. Both One-Sided (OS) and Two-Sided (TS) tests are considered, with reference to: (i) identical success probability (a homogeneous scenario) or (ii) different success probabilities (a non-homogeneous scenario) for the observed samples. As a result of the study, a complete summary (in tabular form) of the relevant statistics for the problem is provided, along with a discussion on the existence of the Uniformly Most Powerful (UMP) test. Finally, when the Likelihood Ratio Test (LRT) is not UMP, existence of the UMP detector after reduction by invariance is investigated.

Decision Fusion With Unknown Sensor Detection Probability

In this letter we study the problem of channel-aware decision fusion when the sensor detection probability is not known at the decision fusion center. Several alternatives proposed in the literature are compared and new fusion rules (namely “ideal sensors” and “locally-optimum detection”) are proposed, showing attractive performance and linear complexity. Simulations are provided to compare the performance of the aforementioned rules.

End-to-end packet-channel Bayesian model applied to heterogeneous wireless networks

This paper proposes a source-traffic based model to estimate jointly packet losses and delays statistical behavior of a network path. The approach relies on a hidden Markov model built on real-traffic information. The effectiveness of the model is evaluated over different real heterogeneous network scenarios. Our experimental results show that the model captures average (long-term) and conditional (short-term) statistics that in most cases are typical of the single scenario. Preliminary results about prediction on a sample path as well as investigation on the use of the same model across different scenarios are given.

Joint Iterative Time-Variant Channel Estimation and Multi-User Detection for MIMO-OFDM Systems

This paper presents an iterative receiver for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. The receiver performs channel estimation and multi-user detection, with soft information iteratively provided by the single-user decoders. Time- variance is effectively taken into account exploiting the properties of the discrete prolate spheroidal (DPS) sequences. Simulation results for the performance are presented in terms of bit error rate (BER) vs signal-to-noise ratio (SNR), showing how the single-user bound (SUB) is approached in a few iterations.