111 research outputs found
Sequentiality and Adaptivity Gains in Active Hypothesis Testing
Consider a decision maker who is responsible to collect observations so as to
enhance his information in a speedy manner about an underlying phenomena of
interest. The policies under which the decision maker selects sensing actions
can be categorized based on the following two factors: i) sequential vs.
non-sequential; ii) adaptive vs. non-adaptive. Non-sequential policies collect
a fixed number of observation samples and make the final decision afterwards;
while under sequential policies, the sample size is not known initially and is
determined by the observation outcomes. Under adaptive policies, the decision
maker relies on the previous collected samples to select the next sensing
action; while under non-adaptive policies, the actions are selected independent
of the past observation outcomes.
In this paper, performance bounds are provided for the policies in each
category. Using these bounds, sequentiality gain and adaptivity gain, i.e., the
gains of sequential and adaptive selection of actions are characterized.Comment: 12 double-column pages, 1 figur
Active sequential hypothesis testing
Consider a decision maker who is responsible to dynamically collect
observations so as to enhance his information about an underlying phenomena of
interest in a speedy manner while accounting for the penalty of wrong
declaration. Due to the sequential nature of the problem, the decision maker
relies on his current information state to adaptively select the most
``informative'' sensing action among the available ones. In this paper, using
results in dynamic programming, lower bounds for the optimal total cost are
established. The lower bounds characterize the fundamental limits on the
maximum achievable information acquisition rate and the optimal reliability.
Moreover, upper bounds are obtained via an analysis of two heuristic policies
for dynamic selection of actions. It is shown that the first proposed heuristic
achieves asymptotic optimality, where the notion of asymptotic optimality, due
to Chernoff, implies that the relative difference between the total cost
achieved by the proposed policy and the optimal total cost approaches zero as
the penalty of wrong declaration (hence the number of collected samples)
increases. The second heuristic is shown to achieve asymptotic optimality only
in a limited setting such as the problem of a noisy dynamic search. However, by
considering the dependency on the number of hypotheses, under a technical
condition, this second heuristic is shown to achieve a nonzero information
acquisition rate, establishing a lower bound for the maximum achievable rate
and error exponent. In the case of a noisy dynamic search with size-independent
noise, the obtained nonzero rate and error exponent are shown to be maximum.Comment: Published in at http://dx.doi.org/10.1214/13-AOS1144 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
A General Class of Throughput Optimal Routing Policies in Multi-hop Wireless Networks
This paper considers the problem of throughput optimal routing/scheduling in
a multi-hop constrained queueing network with random connectivity whose special
case includes opportunistic multi-hop wireless networks and input-queued switch
fabrics. The main challenge in the design of throughput optimal routing
policies is closely related to identifying appropriate and universal Lyapunov
functions with negative expected drift. The few well-known throughput optimal
policies in the literature are constructed using simple quadratic or
exponential Lyapunov functions of the queue backlogs and as such they seek to
balance the queue backlogs across network independent of the topology. By
considering a class of continuous, differentiable, and piece-wise quadratic
Lyapunov functions, this paper provides a large class of throughput optimal
routing policies. The proposed class of Lyapunov functions allow for the
routing policy to control the traffic along short paths for a large portion of
state-space while ensuring a negative expected drift. This structure enables
the design of a large class of routing policies. In particular, and in addition
to recovering the throughput optimality of the well known backpressure routing
policy, an opportunistic routing policy with congestion diversity is proved to
be throughput optimal.Comment: 31 pages (one column), 8 figures, (revision submitted to IEEE
Transactions on Information Theory
Extrinsic Jensen-Shannon Divergence: Applications to Variable-Length Coding
This paper considers the problem of variable-length coding over a discrete
memoryless channel (DMC) with noiseless feedback. The paper provides a
stochastic control view of the problem whose solution is analyzed via a newly
proposed symmetrized divergence, termed extrinsic Jensen-Shannon (EJS)
divergence. It is shown that strictly positive lower bounds on EJS divergence
provide non-asymptotic upper bounds on the expected code length. The paper
presents strictly positive lower bounds on EJS divergence, and hence
non-asymptotic upper bounds on the expected code length, for the following two
coding schemes: variable-length posterior matching and MaxEJS coding scheme
which is based on a greedy maximization of the EJS divergence.
As an asymptotic corollary of the main results, this paper also provides a
rate-reliability test. Variable-length coding schemes that satisfy the
condition(s) of the test for parameters and , are guaranteed to achieve
rate and error exponent . The results are specialized for posterior
matching and MaxEJS to obtain deterministic one-phase coding schemes achieving
capacity and optimal error exponent. For the special case of symmetric
binary-input channels, simpler deterministic schemes of optimal performance are
proposed and analyzed.Comment: 17 pages (two-column), 4 figures, to appear in IEEE Transactions on
Information Theor
Network Attacks Detection by Hierarchical Neural Network
Intrusion detection is an emerging area of research in the computer security and net-works with the growing usage of internet in everyday life. Most intrusion detection systems (IDSs) mostly use a single classifier algorithm to classify the network traffic data as normal behavior or anomalous. However, these single classifier systems fail to provide the best possible attack detection rate with low false alarm rate. In this paper,we propose to use a hybrid intelligent approach using a combination of classifiers in order to make the decision intelligently, so that the overall performance of the resul-tant model is enhanced. The general procedure in this is to follow the supervised or un-supervised data filtering with classifier or cluster first on the whole training dataset and then the output are applied to another classifier to classify the data. In this re- search, we applied Neural Network with Supervised and Unsupervised Learning in order to implement the intrusion detection system. Moreover, in this project, we used the method of Parallelization with real time application of the system processors to detect the systems intrusions.Using this method enhanced the speed of the intrusion detection. In order to train and test the neural network, NSLKDD database was used. Creating some different intrusion detection systems, each of which considered as a single agent, we precisely proceeded with the signature-based intrusion detection of the network.In the proposed design, the attacks have been classified into 4 groups and each group is detected by an Agent equipped with intrusion detection system (IDS).These agents act independently and report the intrusion or non-intrusion in the system; the results achieved by the agents will be studied in the Final Analyst and at last the analyst reports that whether there has been an intrusion in the system or not.
Keywords:
Intrusion Detection, Multi-layer Perceptron, False Positives, Signature- based intrusion detection, Decision tree, Nave Bayes Classifie
Learning-based attacks in cyber-physical systems
We introduce the problem of learning-based attacks in a simple abstraction of
cyber-physical systems---the case of a discrete-time, linear, time-invariant
plant that may be subject to an attack that overrides the sensor readings and
the controller actions. The attacker attempts to learn the dynamics of the
plant and subsequently override the controller's actuation signal, to destroy
the plant without being detected. The attacker can feed fictitious sensor
readings to the controller using its estimate of the plant dynamics and mimic
the legitimate plant operation. The controller, on the other hand, is
constantly on the lookout for an attack; once the controller detects an attack,
it immediately shuts the plant off. In the case of scalar plants, we derive an
upper bound on the attacker's deception probability for any measurable control
policy when the attacker uses an arbitrary learning algorithm to estimate the
system dynamics. We then derive lower bounds for the attacker's deception
probability for both scalar and vector plants by assuming a specific
authentication test that inspects the empirical variance of the system
disturbance. We also show how the controller can improve the security of the
system by superimposing a carefully crafted privacy-enhancing signal on top of
the "nominal control policy." Finally, for nonlinear scalar dynamics that
belong to the Reproducing Kernel Hilbert Space (RKHS), we investigate the
performance of attacks based on nonlinear Gaussian-processes (GP) learning
algorithms
High order algorithms for numerical solution of fractional differential equations
This document is the Accepted Manuscript version of a published work that appeared in final form in [Advances in Difference Equations]. To access the final edited and published work see http://dx.doi.org/10.1186/s13662-021-03273-4.In this paper, two novel high order numerical algorithms are proposed for solving fractional differential equations where the fractional derivative is considered in the Caputo sense. The total domain is discretized into a set of small subdomains and then
the unknown functions are approximated using the piecewise Lagrange interpolation polynomial of degree three and degree four. The detailed error analysis is presented, and it is analytically proven that the proposed algorithms are of orders 4 and 5. The stability of the algorithms is rigorously established and the stability region is also achieved. Numerical examples are provided to check the theoretical results and illustrate the efficiency and applicability of the novel algorithms
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