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1. DATA-DRIVEN DETECTION OF ANOMALIES AND CASCADING FAILURES IN TRAFFIC NETWORKS

   https://doi.org/10.36001/phmconf.2019.v11i1.861  

   Basak, Sanchita ; Ayman, Afiya ; Laszka, Aron ; Dubey, Abhishek ; Leao, Bruno ( September 2019 , Annual Conference of the PHM Society 2019)

   null (Ed.)

   Traffic networks are one of the most critical infrastructures for any community. The increasing integration of smart and connected sensors in traffic networks provides researchers with unique opportunities to study the dynamics of this critical community infrastructure. Our focus in this paper is on the failure dynamics of traffic networks. We are specifically interested in analyzing the cascade effects of traffic congestions caused by physical incidents, focusing on developing mechanisms to isolate and identify the source of a congestion. To analyze failure propagation, it is crucial to develop (a) monitors that can identify an anomaly and (b) a model to capture the dynamics of anomaly propagation. In this paper, we use real traffic data from Nashville, TN to demonstrate a novel anomaly detector and a Timed Failure Propagation Graph based diagnostics mechanism. Our novelty lies in the ability to capture the the spatial information and the interconnections of the traffic network as well as the use of recurrent neural network architectures to learn and predict the operation of a graph edge as a function of its immediate peers, including both incoming and outgoing branches. To study physical traffic incidents, we augment the real data with simulated data generated using SUMO, a microscopic traffic simulator. Our results show that we are able to build LSTM-based traffic-speed predictors with an average loss of 6.55 × 10^−4 compared to Gaussian Process Regression based predictors with an average loss of 1.78 × 10^−2. We are also able to detect anomalies with high precision and recall, resulting in an AUC of 0.8507 for the precision-recall curve. Finally, formulating the cascade propagation problem as a Timed Failure Propagation Graph, we are able to identify the source of a failure accurately. 

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2. Analyzing the Cascading Effect of Traffic Congestion Using LSTM Networks

   https://doi.org/10.1109/BigData47090.2019.9005995  

   Basak, Sanchita ; Dubey, Abhishek ; Bruno, Leao ( December 2019 , IEEE International Conference on Big Data (Big Data))

   This paper presents a data-driven approach for predicting the propagation of traffic congestion at road segments as a function of the congestion in their neighboring segments. In the past, this problem has mostly been addressed by modelling the traffic congestion over some standard physical phenomenon through which it is difficult to capture all the modalities of such a dynamic and complex system. While other recent works have focused on applying a generalized data-driven technique on the whole network at once, they often ignore intersection characteristics. On the contrary, we propose a city-wide ensemble of intersection level connected LSTM models and propose mechanisms for identifying congestion events using the predictions from the networks. To reduce the search space of likely congestion sinks we use the likelihood of congestion propagation in neighboring road segments of a congestion source that we learn from the past historical data. We validated our congestion forecasting framework on the real world traffic data of Nashville, USA and identified the onset of congestion in each of the neighboring segments of any congestion source with an average precision of 0.9269 and an average recall of 0.9118 tested over ten congestion events. 

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3. Cyber Attack and Defense for Smart Inverters in a Distribution System

   Sun, C. C. ; Zhu, R. ; Liu, C. C. ( June 2019 , CIGRE Study Committee D2 Colloquium, Helsinki, Finland)

   The fast-growing installation of solar PVs has a significant impact on the operation of distribution systems. Grid-tied solar inverters provide reactive power capability to support the voltage profile in a distribution system. In comparison with traditional inverters, smart inverters have the capability of real time remote control through digital communication interfaces. However, cyberattack has become a major threat with the deployment of Information and Communications Technology (ICT) in a smart grid. The past cyberattack incidents have demonstrated how attackers can sabotage a power grid through digital communication systems. In the worst case, numerous electricity consumers can experience a major and extended power outage. Unfortunately, tracking techniques are not efficient for today’s advanced communication networks. Therefore, a reliable cyber protection system is a necessary defense tool for the power grid. In this paper, a signature-based Intrusion Detection System (IDS) is developed to detect cyber intrusions of a distribution system with a high level penetration of solar energy. To identify cyberattack events, an attack table is constructed based on the Temporal Failure Propagation Graph (TFPG) technique. It includes the information of potential cyberattack patterns in terms of attack types and time sequence of anomaly events. Once the detected anomaly events are matched with any of the predefined attack patterns, it is judged to be a cyberattack. Since the attack patterns are distinguishable from other system failures, it reduces the false positive rate. To study the impact of cyberattacks on solar devices and validate the performance of the proposed IDS, a realistic Cyber-Physical System (CPS) simulation environment available at Virginia Tech (VT) is used to develop an interconnection between the cyber and power system models. The CPS model demonstrates how communication system anomalies can impact the physical system. The results of two example cyberattack test cases are obtained with the IEEE 13 node test feeder system and the power system simulator, DIgSILENT PowerFactory. 

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4. Traffic Anomaly Detection Via Conditional Normalizing Flow

   https://doi.org/10.1109/ITSC55140.2022.9922061  

   Kang, Zhuangwei ; Mukhopadhyay, Ayan ; Gokhale, Aniruddha ; Wen, Shijie ; Dubey, Abhishek ( October 2022 , 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC))

   Traffic congestion anomaly detection is of paramount importance in intelligent traffic systems. The goals of transportation agencies are two-fold: to monitor the general traffic conditions in the area of interest and to locate road segments under abnormal congestion states. Modeling congestion patterns can achieve these goals for citywide roadways, which amounts to learning the distribution of multivariate time series (MTS). However, existing works are either not scalable or unable to capture the spatial-temporal information in MTS simultaneously. To this end, we propose a principled and comprehensive framework consisting of a data-driven generative approach that can perform tractable density estimation for detecting traffic anomalies. Our approach first clusters segments in the feature space and then uses conditional normalizing flow to identify anomalous temporal snapshots at the cluster level in an unsupervised setting. Then, we identify anomalies at the segment level by using a kernel density estimator on the anomalous cluster. Extensive experiments on synthetic datasets show that our approach significantly outperforms several state-of-the-art congestion anomaly detection and diagnosis methods in terms of Recall and F1-Score. We also use the generative model to sample labeled data, which can train classifiers in a supervised setting, alleviating the lack of labeled data for anomaly detection in sparse settings. 

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5. Generative Anomaly Detection for Time Series Datasets

   Kang, Zhuangwei ; Mukhopadhyay, Ayan ; Gokhale, Aniruddha ; Wen, Shijie and ( January 2022 , ITSC)

   Traffic congestion anomaly detection is of paramount importance in intelligent traffic systems. The goals of transportation agencies are two-fold: to monitor the general traffic conditions in the area of interest and to locate road segments under abnormal congestion states. Modeling congestion patterns can achieve these goals for citywide roadways, which amounts to learning the distribution of multivariate time series (MTS). However, existing works are either not scalable or unable to capture the spatial-temporal information in MTS simultaneously. To this end, we propose a principled and comprehensive framework consisting of a data-driven generative approach that can perform tractable density estimation for detecting traffic anomalies. Our approach first clusters segments in the feature space and then uses conditional normalizing flow to identify anomalous temporal snapshots at the cluster level in an unsupervised setting. Then, we identify anomalies at the segment level by using a kernel density estimator on the anomalous cluster. Extensive experiments on synthetic datasets show that our approach significantly outperforms several state-of-the-art congestion anomaly detection and diagnosis methods in terms of Recall and F1-Score. We also use the generative model to sample labeled data, which can train classifiers in a supervised setting, alleviating the lack of labeled data for anomaly detection in sparse settings. 

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