Ivanov, Radoslav

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Author: Pajic, Miroslav ×

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Now showing 1 - 8 of 8
  • Publication
    Estimation of Blood Oxygen Content Using Context-Aware Filtering
    (2016-04-01) Ivanov, Radoslav; Atanasov, Nikolay; Weimer, James; Simpao, Allan F; Rehman, Mohamed A; Pappas, George; Lee, Insup; Pajic, Miroslav
    In this paper we address the problem of estimating the blood oxygen concentration in children during surgery.Currently, the oxygen content can only be measured through invasive means such as drawing blood from the patient. In this work, we attempt to perform estimation by only using other non-invasive measurements (e.g., fraction of oxygen in inspired air, volume of inspired air) collected during surgery. Although models mapping these measurements to blood oxygen content contain multiple parameters that vary widely across patients, the non-invasive measurements can be used to provide binary information about whether the oxygen concentration is rising or dropping. This information can then be incorporated in a context-aware filter that is used to combine regular continuous measurements with discrete detection events in order to improve estimation. We evaluate the filter using real-patient data collected over the last decade at the Children’s Hospital of Philadelphia and show that it is a promising approach for the estimation of unobservable physiological variables.
  • Publication
    Robust Estimation Using Context-Aware Filtering
    (2015-09-01) Ivanov, Radoslav; Atanasov, Nikolay; Pappas, George; Lee, Insup; Pajic, Miroslav
    This paper presents the context-aware filter, an estimation technique that incorporates context measurements, in addition to the regular continuous measurements. Context measurements provide binary information about the system’s context which is not directly encoded in the state; examples include a robot detecting a nearby building using image processing or a medical device alarming that a vital sign has exceeded a predefined threshold. These measurements can only be received from certain states and can therefore be modeled as a function of the system’s current state. We focus on two classes of functions describing the probability of context detection given the current state; these functions capture a wide variety of detections that may occur in practice. We derive the corresponding context-aware filters, a Gaussian Mixture filter and another closed-form filter with a posterior distribution whose moments are derived in the paper. Finally, we evaluate the performance of both classes of functions through simulation of an unmanned ground vehicle.
  • Publication
    Security of Cyber-Physical Systems in the Presence of Transient Sensor Faults
    (2017-07-01) Park, Junkil; Ivanov, Radoslav; Weimer, James; Lee, Insup; Pajic, Miroslav; Son, Sang Hyuk
    This paper is concerned with the security of modern Cyber-Physical Systems in the presence of transient sensor faults. We consider a system with multiple sensors measuring the same physical variable, where each sensor provides an interval with all possible values of the true state. We note that some sensors might output faulty readings and others may be controlled by a malicious attacker. Different from previous works, in this paper we aim to distinguish between faults and attacks and develop an attack detection algorithm for the latter only. To do this, we note that there are two kinds of faults – transient and permanent; the former are benign and short-lived whereas the latter may have dangerous consequences on system performance.We argue that sensors have an underlying transient fault model that quantifies the amount of time in which transient faults can occur. In addition, we provide a framework for developing such a model if it is not provided by manufacturers. Attacks can manifest as either transient or permanent faults depending on the attacker’s goal. We provide different techniques for handling each kind. For the former, we analyze the worst-case performance of sensor fusion over time given each sensor’s transient fault model and develop a filtered fusion interval that is guaranteed to contain the true value and is bounded in size. To deal with attacks that do not comply with sensors’ transient fault models, we propose a sound attack detection algorithm based on pairwise inconsistencies between sensor measurements. Finally, we provide a real-data case study on an unmanned ground vehicle to evaluate the various aspects of this paper.
  • Publication
    Sensor Attack Detection in the Presence of Transient Faults
    (2015-04-14) Park, Junkil; Ivanov, Radoslav; Weimer, James; Pajic, Miroslav; Lee, Insup
    This paper addresses the problem of detection and identification of sensor attacks in the presence of transient faults. We consider a system with multiple sensors measuring the same physical variable, where some sensors might be under attack and provide malicious values. We consider a setup, in which each sensor provides the controller with an interval of possible values for the true value. While approaches exist for detecting malicious sensor attacks, they are conservative in that they treat attacks and faults in the same way, thus neglecting the fact that sensors may provide faulty measurements at times due to temporary disturbances (e.g., a tunnel for GPS). To address this problem, we propose a transient fault model for each sensor and an algorithm designed to detect and identify attacks in the presence of transient faults. The fault model consists of three aspects: the size of the sensor's interval (1) and an upper bound on the number of errors (2) allowed in a given window size (3). Given such a model for each sensor, the algorithm uses pairwise inconsistencies between sensors to detect and identify attacks. In addition to the algorithm, we provide a framework for selecting a fault model for each sensor based on training data. Finally, we validate the algorithm's performance on real measurement data obtained from an unmanned ground vehicle.
  • Publication
    Attack-Resilient Sensor Fusion for Safety-Critical Cyber-Physical
    (2016-02-01) Ivanov, Radoslav; Pajic, Miroslav; Lee, Insup
    This paper focuses on the design of safe and attack-resilient Cyber-Physical Systems (CPS) equipped with multiple sensors measuring the same physical variable. A malicious attacker may be able to disrupt system performance through compromising a subset of these sensors. Consequently, we develop a precise and resilient sensor fusion algorithm that combines the data received from all sensors by taking into account their specified precisions. In particular, we note that in the presence of a shared bus, in which messages are broadcast to all nodes in the network, the attacker’s impact depends on what sensors he has seen before sending the corrupted measurements. Therefore, we explore the effects of communication schedules on the performance of sensor fusion and provide theoretical and experimental results advocating for the use of the Ascending schedule, which orders sensor transmissions according to their precision starting from the most precise. In addition, to improve the accuracy of the sensor fusion algorithm, we consider the dynamics of the system in order to incorporate past measurements at the current time. Possible ways of mapping sensor measurement history are investigated in the paper and are compared in terms of the confidence in the final output of the sensor fusion. We show that the precision of the algorithm using history is never worse than the no-history one, while the benefits may be significant. Furthermore, we utilize the complementary properties of the two methods and show that their combination results in a more precise and resilient algorithm. Finally, we validate our approach in simulation and experiments on a real unmanned ground robot.
  • Publication
    Resilient Multidimensional Sensor Fusion Using Measurement History
    (2014-02-01) Ivanov, Radoslav; Pajic, Miroslav; Lee, Insup
    This work considers the problem of performing resilient sensor fusion using past sensor measurements. In particular, we consider a system with n sensors measuring the same physical variable where some sensors might be attacked or faulty. We consider a setup in which each sensor provides the controller with a set of possible values for the true value. Here, more precise sensors provide smaller sets. Since a lot of modern sensors provide multidimensional measurements (e.g., position in three dimensions), the sets considered in this work are multidimensional polyhedra. Given the assumption that some sensors can be attacked or faulty, the paper provides a sensor fusion algorithm that obtains a fusion polyhedron which is guaranteed to contain the true value and is minimal in size. A bound on the volume of the fusion polyhedron is also proved based on the number of faulty or attacked sensors. In addition, we incorporate system dynamics in order to utilize past measurements and further reduce the size of the fusion polyhedron. We describe several ways of mapping previous measurements to current time and compare them, under di erent assumptions, using the volume of the fusion polyhedron. Finally, we illustrate the implementation of the best of these methods and show its e ectiveness using a case study with sensor values from a real robot.
  • Publication
    Attack-Resilient Sensor Fusion
    (2014-02-01) Ivanov, Radoslav; Pajic, Miroslav; Lee, Insup
    This work considers the problem of attack-resilient sensor fusion in an autonomous system where multiple sensors measure the same physical variable. A malicious attacker may corrupt a subset of these sensors and send wrong measurements to the controller on their behalf, potentially compromising the safety of the system. We formalize the goals and constraints of such an attacker who also wants to avoid detection by the system. We argue that the attacker’s capabilities depend on the amount of information she has about the correct sensors’ measurements. In the presence of a shared bus where messages are broadcast to all components connected to the network, the attacker may consider all other measurements before sending her own in order to achieve maximal impact. Consequently, we investigate effects of communication schedules on sensor fusion performance. We provide worst- and average-case results in support of the Ascending schedule, where sensors send their measurements in a fixed succession based on their precision, starting from the most precise sensors. Finally, we provide a case study to illustrate the use of this approach.
  • Publication
    Robust Localization Using Context-Aware Filtering
    (2015-07-01) Ivanov, Radoslav; Atanasov, Nikolay; Pajic, Miroslav; Lee, Insup; Pappas, George
    In this paper we develop a robot localization technique that incorporates discrete context measurements, in addition to standard continuous state measurements. Context measurements provide binary information about detected events in the robot’s environment, e.g., a building is recognized using image processing or a known radio station is received. Such measurements can only be detected from certain positions and can, therefore, be correlated with the robot’s state. We investigate two specific examples where context measurements are especially useful – an urban localization scenario where GPS measurements are not reliable as well as the capture of the RQ-170 Sentinel drone in Iran, where GPS measurements were spoofed. By focusing on a specific class of probability of context detection functions, we derive a closed-form Gaussian mixture filter that is precise, captures context, and has the theoretical properties of the Kalman filter. Finally, we provide simulations of the urban localization scenario with an unmanned ground vehicle and show that the proposed context-aware filter is more robust and more accurate than the conventional extended Kalman filter, which only uses continuous measurements.