147 research outputs found
An Effective Approach to Nonparametric Quickest Detection and Its Decentralized Realization
This dissertation focuses on the study of nonparametric quickest detection and its decentralized implementation in a distributed environment. Quickest detection schemes are geared toward detecting a change in the state of a data stream or a real-time process. Classical quickest detection schemes invariably assume knowledge of the pre-change and post-change distributions that may not be available in many applications. A distribution free nonparametric quickest detection procedure is presented based on a novel distance measure, referred to as the Q-Q distance calculated from the Quantile-Quantile plot. Theoretical analysis of the distance measure and detection procedure is presented to justify the proposed algorithm and provide performance guarantees. The Q-Q distance based detection procedure presents comparable performance compared to classical parametric detection procedure and better performance than other nonparametric procedures. The proposed procedure is most effective when detecting small changes. As the technology advances, distributed sensing and detection become feasible. Existing decentralized detection approaches are largely parametric. The decentralized realization of Q-Q distance based nonparametric quickest detection scheme is further studied, where data streams are simultaneously collected from multiple channels located distributively to jointly reach a detection decision. Two implementation schemes, binary quickest detection and local decision fusion, are described. Experimental results show that the proposed method has a comparable performance to the benchmark parametric cumulative sum (CUSUM) test in binary detection. Finally the dissertation concludes with a summary of the contributions to the state of the art
Data-Efficient Quickest Change Detection with On-Off Observation Control
In this paper we extend the Shiryaev's quickest change detection formulation
by also accounting for the cost of observations used before the change point.
The observation cost is captured through the average number of observations
used in the detection process before the change occurs. The objective is to
select an on-off observation control policy, that decides whether or not to
take a given observation, along with the stopping time at which the change is
declared, so as to minimize the average detection delay, subject to constraints
on both the probability of false alarm and the observation cost. By considering
a Lagrangian relaxation of the constraint problem, and using dynamic
programming arguments, we obtain an \textit{a posteriori} probability based
two-threshold algorithm that is a generalized version of the classical Shiryaev
algorithm. We provide an asymptotic analysis of the two-threshold algorithm and
show that the algorithm is asymptotically optimal, i.e., the performance of the
two-threshold algorithm approaches that of the Shiryaev algorithm, for a fixed
observation cost, as the probability of false alarm goes to zero. We also show,
using simulations, that the two-threshold algorithm has good observation
cost-delay trade-off curves, and provides significant reduction in observation
cost as compared to the naive approach of fractional sampling, where samples
are skipped randomly. Our analysis reveals that, for practical choices of
constraints, the two thresholds can be set independent of each other: one based
on the constraint of false alarm and another based on the observation cost
constraint alone.Comment: Preliminary version of this paper has been presented at ITA Workshop
UCSD 201
A distributed Self-adaptive Nonparametric Change-Detection Test for Sensor/Actuator Networks
Abstract. The prompt detection of faults and, more in general, changes in stationarity in networked systems such as sensor/actuator networks is a key issue to guarantee robustness and adaptability in applications working in reallife environments. Traditional change-detection methods aiming at assessing the stationary of a data generating process would require a centralized availability of all observations, solution clearly unacceptable when large scale networks are considered and data have local interest. Differently, distributed solutions based on decentralized change-detection tests exploiting information at the unit and cluster level would be a solution. This work suggests a novel distributed change-detection test which operates at two-levels: the first, running on the unit, is particularly reactive in detecting small changes in the process generating the data, whereas the second exploits distributed information at the cluster-level to reduce false positives. Results can be immediately integrated in the machine learning community where adaptive solutions are envisaged to address changes in stationarity of the considered application. A large experimental campaign shows the effectiveness of the approach both on synthetic and real data applications.
Quickest Detection of Denial-of-Service Attacks in Cognitive Wireless Networks
Abstract Many denial-of-service (DOS) attacks in wireless networks, such as jamming, will cause significant performance degradation to the network and thus need to be detected quickly. This becomes more important in a cognitive wireless network employing dynamic spectrum access (DSA), where it is easier for the attackers to launch DOS attacks. For instance, the attackers may pretend to be a licensed primary user, and carry out the primary user emulation (PUE) attacks. The attackers may also explore the spectrum themselves, and conduct smart jamming. These attacks usually happen at unknown time and are unpredictable due to the lack of prior knowledge of the attackers. It is also observed that the statistical property of the resulted paths from multipath routing will have abrupt change when the attack happens. Hence, in this paper, we formulate the detection of DOS attacks as a quickest detection problem, i.e., detect the abrupt changes in distributions of certain observables at the network layer with minimum detection delay, while maintaining a given low false alarm probability. Specifically, we propose a non-parametric version of the Pages cumulative sum (CUSUM) algorithm to minimize the detection delay so that a network manager may react to the event as soon as possible to mitigate the effect of the attacks. Simulation results using a Spectrum-Aware Split Multipath Routing with dynamic channel assignment as a baseline routing protocol demonstrate the effectiveness of the proposed approach
Secure Distributed Dynamic State Estimation in Wide-Area Smart Grids
Smart grid is a large complex network with a myriad of vulnerabilities,
usually operated in adversarial settings and regulated based on estimated
system states. In this study, we propose a novel highly secure distributed
dynamic state estimation mechanism for wide-area (multi-area) smart grids,
composed of geographically separated subregions, each supervised by a local
control center. We firstly propose a distributed state estimator assuming
regular system operation, that achieves near-optimal performance based on the
local Kalman filters and with the exchange of necessary information between
local centers. To enhance the security, we further propose to (i) protect the
network database and the network communication channels against attacks and
data manipulations via a blockchain (BC)-based system design, where the BC
operates on the peer-to-peer network of local centers, (ii) locally detect the
measurement anomalies in real-time to eliminate their effects on the state
estimation process, and (iii) detect misbehaving (hacked/faulty) local centers
in real-time via a distributed trust management scheme over the network. We
provide theoretical guarantees regarding the false alarm rates of the proposed
detection schemes, where the false alarms can be easily controlled. Numerical
studies illustrate that the proposed mechanism offers reliable state estimation
under regular system operation, timely and accurate detection of anomalies, and
good state recovery performance in case of anomalies
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