Online (also called "recursive" or "adaptive") estimation of fixed model
parameters in hidden Markov models is a topic of much interest in times series
modelling. In this work, we propose an online parameter estimation algorithm
that combines two key ideas. The first one, which is deeply rooted in the
Expectation-Maximization (EM) methodology consists in reparameterizing the
problem using complete-data sufficient statistics. The second ingredient
consists in exploiting a purely recursive form of smoothing in HMMs based on an
auxiliary recursion.

We propose a non-parametric statistical procedure for detecting multiple
change-points in multidimensional signals. The method is based on a test
statistic that generalizes the well-known Kruskal-Wallis procedure to the
multivariate setting. The proposed approach does not require any knowledge
about the distribution of the observations and is parameter-free. It is
computationally efficient thanks to the use of dynamic programming and can also
be applied when the number of change-points is unknown.

We propose a novel approach for distributed statistical detection of
change-points in high-volume network traffic. We consider more specifically the
task of detecting and identifying the targets of Distributed Denial of Service
(DDoS) attacks. The proposed algorithm, called DTopRank, performs distributed
network anomaly detection by aggregating the partial information gathered in a
set of network monitors.