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1. Distributionally Robust Quickest Change Detection using Wasserstein Uncertainty Sets

   Xie, Liyan; Liang, Yuchen; Veeravalli, Venugopal V (May 2024, PMLR)

   Dasgupta, Sanjoy; Mandt, Stephan; Li, Yingzhen (Ed.)

   The problem of quickest detection of a change in the distribution of streaming data is considered. It is assumed that the pre-change distribution is known, while the only information about the post-change is through a (small) set of labeled data. This post-change data is used in a data-driven minimax robust framework, where an uncertainty set for the post-change distribution is constructed. The robust change detection problem is studied in an asymptotic setting where the mean time to false alarm goes to infinity. It is shown that the least favorable distribution (LFD) is an exponentially tilted version of the pre-change density and can be obtained efficiently. A Cumulative Sum (CuSum) test based on the LFD, which is referred to as the distributionally robust (DR) CuSum test, is then shown to be asymptotically robust. The results are extended to the case with multiple post-change uncertainty sets and validated using synthetic and real data examples. 

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2. Optimal Quickest Change Detection in Sensor Networks Using Ordered Transmissions

   https://doi.org/10.1109/SPAWC48557.2020.9154270  

   Chen, Yicheng; Blum, Rick S.; Sadler, Brian M. (May 2020, 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC))

   null (Ed.)

   Quickest change detection in a sensor network is considered where each sensor observes a sequence of random variables and transmits its local information on the observations to a fusion center. At an unknown point in time, the distribution of the observations at all sensors changes. The objective is to detect the change in distribution as soon as possible, subject to a false alarm constraint. We consider minimax formulations for this problem and propose a new approach where transmissions are ordered and halted when sufficient information is accumulated at the fusion center. We show that the proposed approach can achieve the optimal performance equivalent to the centralized cumulative sum (CUSUM) algorithm while requiring fewer sensor transmissions. Numerical results for a shift in mean of independent and identically distributed Gaussian observations show significant communication savings for the case where the change seldom occurs which is frequently true in many important applications. 

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3. RSU-Based Online Intrusion Detection and Mitigation for VANET

   https://doi.org/10.3390/s22197612  

   Haydari, Ammar; Yilmaz, Yasin (October 2022, Sensors)

   Secure vehicular communication is a critical factor for secure traffic management. Effective security in intelligent transportation systems (ITS) requires effective and timely intrusion detection systems (IDS). In this paper, we consider false data injection attacks and distributed denial-of-service (DDoS) attacks, especially the stealthy DDoS attacks, targeting integrity and availability, respectively, in vehicular ad-hoc networks (VANET). Novel machine learning techniques for intrusion detection and mitigation based on centralized communications through roadside units (RSU) are proposed for the considered attacks. The performance of the proposed methods is evaluated using a traffic simulator and a real traffic dataset. Comparisons with the state-of-the-art solutions clearly demonstrate the superior detection and localization performance of the proposed methods by 78% in the best case and 27% in the worst case, while achieving the same level of false alarm probability. 

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4. Quickest Detection of Series Arc Faults on DC Microgrids

   https://doi.org/10.1109/ECCE47101.2021.9595315  

   Gajula, Kaushik; Le, Vu; Yao, Xiu; Zou, Shaofeng; Herrera, Luis (October 2021, IEEE Energy Conversion Congress and Expo (ECCE))

   In this paper we explore the problem of series arc fault detection and localization on dc microgrids. Through a statistical model of the microgrid obtained by nodal equation, the injection currents are modeled as a random vector whose distribution depends on the nodal voltages and the admittance matrix. A series arc fault causes a change in the admittance matrix, which further leads to a change in the data generating distribution of injection currents. The goal is to detect and localize faults on different lines in a timely fashion subject to false alarm constraints. The model is formulated as a quickest change detection problem, and the classical Cumulative Sum algorithm (CUSUM) is employed. The proposed framework is tested on a dc microgrid with active (constant power) loads. Furthermore, a case considering fault detection in the presence of an internal node is presented. Finally, we present an experimental result on a four node dc microgrid to verify the practical application of our approach. 

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5. A Feature Selection Method Based on Shapley Value to False Alarm Reduction in ICUs A Genetic-Algorithm Approach

   https://doi.org/10.1109/EMBC.2018.8512266  

   Zaeri-Amirani, Mohammad; Afghah, Fatemeh; Mousavi, Sajad (July 2018, A Feature Selection Method Based on Shapley Value to False Alarm Reduction in ICUs A Genetic-Algorithm Approach)

   High false alarm rate in intensive care units (ICUs) has been identified as one of the most critical medical challenges in recent years. This often results in overwhelming the clinical staff by numerous false or unurgent alarms and decreasing the quality of care through enhancing the probability of missing true alarms as well as causing delirium, stress, sleep deprivation and depressed immune systems for patients. One major cause of false alarms in clinical practice is that the collected signals from different devices are processed individually to trigger an alarm, while there exists a considerable chance that the signal collected from one device is corrupted by noise or motion artifacts. In this paper, we propose a low-computational complexity yet accurate game-theoretic feature selection method which is based on a genetic algorithm that identifies the most informative biomarkers across the signals collected from various monitoring devices and can considerably reduce the rate of false alarms. 

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