Abstract:
Cyber attacks and malicious activities are rapidlybecoming a major threat to proper secure organization.Many security tools may be installed in distributed systemsand monitor all events in a network. Security managers oftenhave to process huge numbers of alerts per day, produced bysuch tools. Intrusion prediction is an important technique tohelp response systems reacting properly before the networkis compromised. In this paper, we propose a frameworkto predict multi-step attacks before they pose a serioussecurity risk. Hidden Markov Model (HMM) is used toextract the interactions between attackers and networks.Since alerts correlation plays a critical role in prediction,a modulated alert severity through correlation concept isused instead of just individual alerts and their severity.Modulated severity generates prediction alarms for the mostinteresting steps of multi-step attacks and improves theaccuracy. Our experiments on the Lincoln Laboratory 2000data set show that our algorithm perfectly predicts multi-step attacks before they can compromise the network.

Abstract:
Nowadays, mobile service requests in wireless networking are aimed to the benefit of a good level of satisfaction for the received Quality of Service (QoS) guarantees. In this paper, a new prediction algorithm is proposed, for the pre-reservation of passive bandwidth, when mobile users moves under radio coverage that can be considered as a cellular one (GSM, UMTS, WLAN clusters, etc.). The Hidden Markov Chains (HMC) theory is used to design the predictor, as the main component of the proposed idea, that does not depend on the considered transmission technology, mobility model or vehicular scenario. Mobile ReSerVation Protocol (MRSVP) has been used in order to realize the active/passive bandwidth reservation in the considered network topology. Different simulation campaigns have been carried out in order to appreciate the benefits of the proposed idea.

Abstract:
Hidden Markov models (HMMs) are probabilistic functions of finite Markov chains, or, put in other words, state space models with finite state space. In this paper, we examine subspace estimation methods for HMMs whose output lies a finite set as well. In particular, we study the geometric structure arising from the nonminimality of the linear state space representation of HMMs, and consistency of a subspace algorithm arising from a certain factorization of the singular value decomposition of the estimated linear prediction matrix. For this algorithm, we show that the estimates of the transition and emission probability matrices are consistent up to a similarity transformation, and that the $m$-step linear predictor computed from the estimated system matrices is consistent, i.e., converges to the true optimal linear $m$-step predictor.

Abstract:
We propose a nonlinear filtering framework for approaching the problems of channel state tracking and spatiotemporal channel gain prediction in mobile wireless sensor networks, in a Bayesian setting. We assume that the wireless channel constitutes an observable (by the sensors/network nodes), spatiotemporal, conditionally Gaussian stochastic process, which is statistically dependent on a set of hidden channel parameters, called the channel state. The channel state evolves in time according to a known, non stationary, nonlinear and/or non Gaussian Markov stochastic kernel. This formulation results in a partially observable system, with a temporally varying global state and spatiotemporally varying observations. Recognizing the intractability of general nonlinear state estimation, we advocate the use of grid based approximate filters as an effective and robust means for recursive tracking of the channel state. We also propose a sequential spatiotemporal predictor for tracking the channel gains at any point in time and space, providing real time sequential estimates for the respective channel gain map, for each sensor in the network. Additionally, we show that both estimators converge towards the true respective MMSE optimal estimators, in a common, relatively strong sense. Numerical simulations corroborate the practical effectiveness of the proposed approach.

Abstract:
Since membranous proteins play a key role in drug targeting therefore transmembrane proteins prediction is active and challenging area of biological sciences. Location based prediction of transmembrane proteins are significant for functional annotation of protein sequences. Hidden markov model based method was widely applied for transmembrane topology prediction. Here we have presented a revised and a better understanding model than an existing one for transmembrane protein prediction. Scripting on MATLAB was built and compiled for parameter estimation of model and applied this model on amino acid sequence to know the transmembrane and its adjacent locations. Estimated model of transmembrane topology was based on TMHMM model architecture. Only 7 super states are defined in the given dataset, which were converted to 96 states on the basis of their length in sequence. Accuracy of the prediction of model was observed about 74 %, is a good enough in the area of transmembrane topology prediction. Therefore we have concluded the hidden markov model plays crucial role in transmembrane helices prediction on MATLAB platform and it could also be useful for drug discovery strategy.

Abstract:
There are many situations in which it would be beneficial for a robot to have predictive abilities similar to those of rational humans. Some of these situations include collaborative robots, robots in adversarial situations, and for dynamic obstacle avoidance. This paper presents an approach to modeling behaviors of dynamic agents in order to empower robots with the ability to predict the agent's actions and identify the behavior the agent is executing in real time. The method of behavior modeling implemented uses hidden Markov models (HMMs) to model the unobservable states of the dynamic agents. The background and theory of the behavior modeling is presented. Experimental results of realistic simulations of a robot predicting the behaviors and actions of a dynamic agent in a static environment are presented.

Abstract:
Our HMM is designed without prior knowledge. It is chosen within a collection of models of increasing size, using statistical and accuracy criteria. The resulting model has 36 hidden states: 15 that model α-helices, 12 that model coil and 9 that model β-strands. Connections between hidden states and state emission probabilities reflect the organization of protein structures into secondary structure segments. We start by analyzing the model features and see how it offers a new vision of local structures. We then use it for secondary structure prediction. Our model appears to be very efficient on single sequences, with a Q3 score of 68.8%, more than one point above PSIPRED prediction on single sequences. A straightforward extension of the method allows the use of multiple sequence alignments, rising the Q3 score to 75.5%.The hidden Markov model presented here achieves valuable prediction results using only a limited number of parameters. It provides an interpretable framework for protein secondary structure architecture. Furthermore, it can be used as a tool for generating protein sequences with a given secondary structure content.Predicting the secondary structure of a protein is often a first step toward 3D structure prediction of a particular protein. In comparative modeling, secondary structure prediction is used to refine sequence alignments, or to improve the detection of distant homologs [1]. Moreover, it is of prime importance when prediction is made without a template [2]. For all these reasons protein secondary structure prediction has remained an active field for years. Virtually all statistical and learning methods have been applied to this task. Nowadays, the best methods achieve prediction rate of about 80% using homologous sequence information. A survey of the Eva on-line evaluation [3] shows that the top performing methods include several approaches based on neural networks, e.g. PSIPRED by Jones et al [4], PROFsec and PHDpsi by Rost et al [5]. Recentl

Abstract:
This study mainly deals with a general framework for Hidden Markov Models and Neural Networks by using back propagation algorithm. In the training phase an efficient way of updating the weights of neurons based on the relative entopy function is introduced, so that the network converges in a rapid manner. Exponential gradient descent algorithm has been used for updating the weights of neurons in each iteration of the training phase. Here the component of the gradient term appears in the exponent of a factor that is used in updating the weight vector multiplicatively. A scaling factor is derived in which the hidden layer output is linear which represents the total weight on hidden layer nodes. The numbers of hidden layer units were also varied for the various learning rates and the performance were marked. The efficiency and accuracy of learning process greatly depends on the training algorithm used. An algorithm is designed in such a way that a new weight update rule has been introduced. Exponentiated gradient descent weight update rate that substitutes the existing gradient descent update rate of back propagation algorithm in which the results are faster and it is an efficient Markov learning network. The Hidden Neural Network can be viewed as an undirected probabilistic model in which the study of the structure of the standard genetic code is analyzed.

Agents interactions in a social network are dynamic and stochastic. We model
the dynamic interactions using the hidden Markov model, a probability model
which has a wide array of applications. The transition matrix with three states,
forgetting, reinforcement and exploration is estimated using simulation. Singular
value decomposition estimates the observation matrix for emission of
low, medium and high interaction rates. This is achieved when the rank approximation
is applied to the transition matrix. The initial state probabilities
are then estimated with rank approximation of the observation matrix. The
transition and the observation matrices estimate the state and observed symbols
in the model. Agents interactions in a social network account for between
20% and 50% of all the activities in the network. Noise contributes to the other
portion due to interaction dynamics and rapid changes observable from the
agents transitions in the network. In the model, the interaction proportions
are low with 11%, medium with 56% and high with 33%. Hidden Markov
model has a strong statistical and mathematical structure to model interactions
in a social network.

Abstract:
A wireless sensor network is a network that is made of hundreds or thousands of sensor nodes which are densely deployed in an unattended environment with the capabilities of sensing, wireless communications and computations which collects and disseminates environmental data. For many applications in wireless sensor networks, users may want to continuously extract data from the networks for analysis later. However, accurate data extraction is difficult and it is often too costly to obtain all sensor readings, as well as not necessary in the sense that the readings themselves only represent samples of the true state of the world.Energy conservation is crucial to the prolonged lifetime of a sensor network. Energy consumption can be reduced for data collections from sensor nodes using prediction. The prediction based algorithms are based on the observation that the sensor capable of local computation generates the possibility of training and using predictors in a distributed way.An energy efficient framework for clustering based data collection in wireless sensor networks can be done by adaptively integrating enabling/disabling prediction scheme with sleep/awake. The framework consists of a number of sensor nodes which form clusters. Each cluster has a cluster head and set of sensor nodes attached to it. Cluster head collects the data value from its member nodes .The prediction incorporated in the member nodes imply that sensors need not to transmit the data if it does not differ from a predicted value by a certain threshold. A member need not be awake if no data value has to be transmitted but only has to periodically check the data values and awake only if it differs from the predicted value. If prediction is disabled it simply transmits the data values. The performance of power saving in clustering based prediction is evaluated by creating a network scenario for tracking a moving object in NS-2.33 simulator.