| 1. |
- Naha, Arunava, et al.
(författare)
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Bayesian Quickest Change-Point Detection With an Energy Harvesting Sensor and Asymptotic Analysis
- 2024
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Ingår i: IEEE Transactions on Signal Processing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1053-587X .- 1941-0476. ; 72, s. 565-579
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Tidskriftsartikel (refereegranskat)abstract
- This paper studies the problem of the quickest change-point detection by a sensor powered by randomly available energy harvested from the environment under a Bayesian framework. In particular, the sensor observes a stochastic process by taking and processing samples at discrete times. We assume the distribution of the sampled data changes at an unknown random time, and the pre and post-change distributions are stationary and known. In the proposed framework, the sensor takes a new sample if there is enough evidence of a change. Otherwise, the sensor saves energy for the future and does not take new samples. The optimal policy is obtained by dynamic programming that minimizes the average detection delay for fixed upper bounds on the false alarm rate and an average number of samples taken before the change point. We model the test statistics as a perturbed random walk and study the asymptotic performance of the proposed method applying non-linear renewal theory under two different scenarios. First, (H) over bar >= E-s, where (H) over bar is the average harvested energy in one sampling period and E-s is the energy needed to take and process a new sample, and second, (H) over bar < E-s. For the first scenario, the optional policy turns out to be greedy, i.e., the sensor takes samples when sufficient energy is available. However, under the second scenario, the proposed method performs better than the greedy approach since it prepares for the future and uses available energy parsimoniously. We have provided several numerical results to support the derived theory.
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| 2. |
- Naha, Arunava, et al.
(författare)
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Deception Attack Detection using Reduced Watermarking
- 2021
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Ingår i: ECC 2021. - : Institute of Electrical and Electronics Engineers (IEEE). - 9789463842365 - 9781665479455 ; , s. 74-80
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Konferensbidrag (refereegranskat)abstract
- The addition of physical watermarking to the control input is a well-adopted technique to detect the data deception attacks on the cyber-physical systems. However, the addition of the watermarking increases the control cost. On the other hand, the attack might be a rare event. In this paper, we propose to reduce the control cost when the system is not under attack by adding the watermarking as and when needed depending on a posterior probability of attack. We first formulate a stochastic optimal control problem, and then solve it using dynamic programming by keeping a balance between the detection delay, false alarm rate (FAR), and the reduction in control cost. We numerically find two thresholds from the value iterations, Th e and Th d , Th d is greater than Th e , for the posterior probability of attack p k . If p k is greater than or equal to Th e , then the watermarking signal is added for the (k+1)-th instant of time. On the other hand, if p k greater than or equal to Th d , then we declare that the system is under attack. We have provided simulation results to illustrate our approach. For the example system model considered in this paper, we have achieved a considerable reduction in the control cost during the normal operation compared to the case where watermarking is always present without sacrificing much in the detection delay.
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| 3. |
- Naha, Arunava, et al.
(författare)
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Quickest detection of deception attacks on cyber-physical systems with a parsimonious watermarking policy
- 2023
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Ingår i: Automatica. - : Elsevier BV. - 0005-1098 .- 1873-2836. ; 155
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Tidskriftsartikel (refereegranskat)abstract
- Adding a physical watermarking signal to the control input of a networked control system increases the detection probability of data deception attacks at the expense of increased control cost. This paper proposes a parsimonious policy to limit the average number of watermarking events when the attack is not present, which in turn reduces the control cost. We model the system as a stochastic optimal control problem and apply dynamic programming to minimize the average detection delay (ADD) for fixed upper bounds on false alarm rate (FAR) and an average number of watermarking events (ANW) before the attack. Under practical circumstances, the optimal solution results in a two threshold policy on the posterior probability of attack, derived from the Shiryaev statistics for sequential change detection and assuming the change point is a random variable. We derive asymptotically approximate analytical expressions of ADD and FAR, applying the non-linear renewal theory for non-independent and identically distributed data. The derived expressions reveal that ADD reduces with the increase in the Kullback-Leibler divergence (KLD) between the post-and pre-attack distributions of the test statistics. Therefore, we further design the optimal watermarking that maximizes the KLD for a fixed increase in the control cost. The relationship between the ANW and the increase in control cost is also derived. Simulation studies are performed to illustrate and validate the theoretical results.
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| 4. |
- Naha, Arunava, et al.
(författare)
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Quickest physical watermarking-based detection of measurement replacement attacks in networked control systems
- 2023
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Ingår i: European Journal of Control. - : Elsevier. - 0947-3580 .- 1435-5671. ; 71
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we propose and analyze an attack detection scheme for securing the physical layer of a networked control system (NCS) with a wireless sensor network against attacks where the adversary replaces the true observations with stationary false data. An independent and identically distributed watermarking signal is added to the optimal linear quadratic Gaussian (LQG) control inputs, and a cumulative sum (CUSUM) test is carried out using the joint distribution of the innovation signal and the watermarking signal for quickest attack detection. We derive the expressions of the supremum of the average detection delay (SADD) for a multi-input and multi-output (MIMO) system under the optimal and sub-optimal CUSUM tests. The SADD is asymptotically inversely proportional to the expected Kullback–Leibler divergence (KLD) under certain conditions. The expressions for the MIMO case are simplified for multi-input and single-output systems and explored further to distil design insights. We provide insights into the design of an optimal watermarking signal to maximize KLD for a given fixed increase in LQG control cost when there is no attack. Furthermore, we investigate how the attacker and the control system designer can accomplish their respective objectives by changing the relative power of the attack signal and the watermarking signal. Simulations and numerical studies are carried out to validate the theoretical results.
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| 5. |
- Naha, Arunava, et al.
(författare)
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Sequential detection of Replay attacks
- 2023
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Ingår i: IEEE Transactions on Automatic Control. - : IEEE. - 0018-9286 .- 1558-2523 .- 2334-3303. ; 68:3, s. 1941-1948
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Tidskriftsartikel (refereegranskat)abstract
- One of the most studied forms of attacks on the cyber-physical systems is the replay attack. The statistical similarities of the replayed signal and the true observations make the replay attack difficult to detect. In this article, we address the problem of replay attack detection by adding watermarking to the control inputs and then perform resilient detection using cumulative sum (CUSUM) test on the joint statistics of the innovation signal and the watermarking signal, whereas existing work considers only the marginal distribution of the innovation signal. We derive the expression of the Kullback-Liebler divergence (KLD) between the two joint distributions before and after the replay attack, which is, asymptotically, inversely proportional to the detection delay. We perform a structural analysis of the derived KLD expression and suggest a technique to improve the KLD for the systems with relative degree greater than one. A scheme to find the optimal watermarking signal variance for a fixed increase in the control cost to maximize the KLD under the CUSUM test is presented. We provide various numerical simulation results to support our theory. The proposed method is also compared with a state-of-the-art method based on the Neyman-Pearson detector, illustrating the smaller detection delay of the proposed sequential detector.
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| 6. |
- Naha, Arunava, et al.
(författare)
-
Sequential Detection of Replay Attacks with a Parsimonious Watermarking Policy
- 2022
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Ingår i: 2022 American Control Conference (ACC). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665451963 - 9781665494809 - 9781665451970 ; , s. 4868-4875
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Konferensbidrag (refereegranskat)abstract
- In this paper, we have proposed a technique for Bayesian sequential detection of replay attacks on networked control systems with a constraint on the average number of watermarking (ANW) events used during normal system operations. Such a constraint limits the increase in the control cost due to watermarking. To determine the optimal sequence regarding the addition or otherwise of watermarking signals, first, we formulate an infinite horizon stochastic optimal control problem with a termination state. Then applying the value iteration approach, we find an optional policy that minimizes the average detection delay (ADD) for fixed upper bounds on the false alarm rate (FAR) and ANW. The optimal policy turns out to be a two thresholds policy on the posterior probability of attack. We derive approximate expressions of ADD and FAR as functions of the two derived thresholds and a few other parameters. A simulation study on a single-input single-output system illustrates that the proposed method improves the control cost considerably at the expense of small increases in ADD. We also perform simulation studies to validate the derived theoretical results.
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| 7. |
- Naha, Arunava, et al.
(författare)
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Structural analyses of a parsimonious watermarking policy for data deception attack detection in networked control systems
- 2022
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Ingår i: 2022 IEEE 61st Conference on Decision and Control (CDC). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665467612 - 9781665467605 - 9781665467629 ; , s. 7648-7655
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Konferensbidrag (refereegranskat)abstract
- In this paper, we perform structural analyses of a parsimonious watermarking policy, which minimizes the average detection delay (ADD) to detect data deception attacks on networked control systems (NCS) for a fixed upper bound on the false alarm rate (FAR). The addition of physical watermarking to the control input of a NCS increases the probability of attack detections with an increase in the control cost. Therefore, we formulate the problem of data deception attack detection for NCS with the facility to add physical watermarking as a stochastic optimal control problem. Then we solve the problem by applying dynamic programming value iterations and find a parsimonious watermarking policy that decides to add watermarking and detects attacks based on the estimated posterior probability of attack. We analyze the optimal policy structure and find that it can be a one, two or three threshold policy depending on a few parameter values. Simulation studies show that the optimal policy for a practical range of parameter values is a two-threshold policy on the posterior probability of attack. Derivation of a threshold-based policy from the structural analysis of the value iteration method reduces the computational complexity during the runtime implementation and offers better structural insights. Furthermore, such an analysis provides a guideline for selecting the parameter values to meet the design requirements.
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| 8. |
- Samanta, Anik Kumar, et al.
(författare)
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Minimum Distance-Based Detection of Incipient Induction Motor Faults Using Rayleigh Quotient Spectrum of Conditioned Vibration Signal
- 2021
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Ingår i: IEEE Transactions on Instrumentation and Measurement. - : Institute of Electrical and Electronics Engineers (IEEE). - 0018-9456 .- 1557-9662. ; 70
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Tidskriftsartikel (refereegranskat)abstract
- In this article, we propose a single-vibration sensor-based method for detecting incipient faults in squirrel cage induction motors (SCIMs). We consider defects in different parts of the bearing (inner raceway, outer raceway, cage train, and rolling element) and in a single bar of the rotor. The vibration signal is dominated by the fundamental rotational frequency and its harmonics. The dominant components result in numerical errors while estimating the relatively indistinct fault-specific spectral components. In this article, we precondition the vibration signal by suppressing multiple dominant components using an extended Kalman filter-based method. The suppression of the dominant components reduces the spectral leakage, exposes minute fault components, and improves the overall amplitude estimation. Subsequently, we estimate the fault frequency and amplitude using an accurate and low-complexity Rayleigh-quotient-based spectral estimator. The thresholds for fault detection are determined from a small number of healthy data, and an adaptive minimum distance-based detector is used for hypothesis testing. The proposed test improves detection and reduces false alarms under noisy conditions. We test the complete algorithm using data from a 22-kW SCIM laboratory setup. The proposed method has achieved 100% accuracy with the publicly available 12-kHz drive-end bearing data from Case Western Reserve University, Cleveland, OH, USA.
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| 9. |
- Zhao, Ziwen, et al.
(författare)
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Automated Analysis of Nano-Impact Single-Entity Electrochemistry Signals Using Unsupervised Machine Learning and Template Matching
- 2024
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Ingår i: ADVANCED INTELLIGENT SYSTEMS. - : John Wiley & Sons. - 2640-4567. ; 6:1
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Tidskriftsartikel (refereegranskat)abstract
- Nano-impact (NIE) (also referred to as collision) single-entity electrochemistry is an emerging technique that enables electrochemical investigation of individual entities, ranging from metal nanoparticles to single cells and biomolecules. To obtain meaningful information from NIE experiments, analysis and feature extraction on large datasets are necessary. Herein, a method is developed for the automated analysis of NIE data based on unsupervised machine learning and template matching approaches. Template matching not only facilitates downstream processing of the NIE data but also provides a more accurate analysis of the NIE signal characteristics and variations that are difficult to discern with conventional data analysis techniques, such as the height threshold method. The developed algorithm enables fast automated processing of large experimental datasets recorded with different systems, requiring minimal human intervention and thereby eliminating human bias in data analysis. As a result, it improves the standardization of data processing and NIE signal interpretation across various experiments and applications. Nano-impact (NIE) electrochemistry is an emerging technique for studying individual entities. Analyzing large NIE datasets, often with low signal-to-noise ratios, is challenging. Herein, an automated approach is introduced using unsupervised machine learning and template matching for accurate feature extraction from spike-shaped NIE signals. It improves data processing, accuracy and standardization, reducing human bias in signal interpretation across experiments.image (c) 2023 WILEY-VCH GmbH
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