Semi-Markov Switching Vector Autoregressive Model-Based Anomaly Detection in Aviation Systems

Melnyk, Igor; Banerjee, Arindam; Matthews, Bryan; Oza, Nikunj C.

doi:10.1145/2939672.2939789

Cited by 45 publications

(23 citation statements)

References 27 publications

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“…The anomaly scores are finally determined based on the residuals. Inside this category, we can include anomaly detection techniques based on traditional time series forecasting models such as Vector Auto-Regressive (VAR) [38,39] and Autoregressive Integrated Moving Average (ARIMA) [40,41]. Also, RNN have been used as regression models and will be covered later on in a specific section of the survey.…”

Section: Regression Model-basedmentioning

confidence: 99%

“…In another framework also based on VAR modelling and adapted to online anomaly, Melnyk et al [39] represent each flight with a semi-Markov switching vector autoregressive (SMS-VAR) model. With this approach, each phase of a flight determined by the set of pilot switches is represented by a different VAR process [115] and a semi-Markov model (SMM) [116] is used for the dynamics of flight switches.…”

Section: Statistical-based Approachesmentioning

confidence: 99%

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Recent Advances in Anomaly Detection Methods Applied to Aviation

2019

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Anomaly detection is an active area of research with numerous methods and applications. This survey reviews the state-of-the-art of data-driven anomaly detection techniques and their application to the aviation domain. After a brief introduction to the main traditional data-driven methods for anomaly detection, we review the recent advances in the area of neural networks, deep learning and temporal-logic based learning. In particular, we cover unsupervised techniques applicable to time series data because of their relevance to the aviation domain, where the lack of labeled data is the most usual case, and the nature of flight trajectories and sensor data is sequential, or temporal. The advantages and disadvantages of each method are presented in terms of computational efficiency and detection efficacy. The second part of the survey explores the application of anomaly detection techniques to aviation and their contributions to the improvement of the safety and performance of flight operations and aviation systems. As far as we know, some of the presented methods have not yet found an application in the aviation domain. We review applications ranging from the identification of significant operational events in air traffic operations to the prediction of potential aviation system failures for predictive maintenance.

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Section: Regression Model-basedmentioning

confidence: 99%

Section: Statistical-based Approachesmentioning

confidence: 99%

Recent Advances in Anomaly Detection Methods Applied to Aviation

2019

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show abstract

Section: Regression Model-basedmentioning

confidence: 99%

Recent Advances in Anomaly Detection Methods applied to Aviation

Basora¹,

Olivé²,

Dubot³

2019

Preprint

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Anomaly detection is an active area of research with numerous methods and applications. This survey reviews the state-of-the-art of data-driven anomaly detection techniques and their application to the the aviation domain. After a brief introduction to the main traditional data-driven methods for anomaly detection, we review the recent advances in the area of neural networks, deep learning and temporal-logic based learning. We cover especially unsupervised techniques applicable to time series data because of their relevance to the aviation domain, where the lack of labeled data is the most usual case, and the nature of flight trajectories and sensor data is sequential, or temporal. The advantages and disadvantages of each method are presented in terms of computational efficiency and detection efficacy. The second part of the survey explores the application of anomaly detection techniques to aviation and their contributions to the improvement of the safety and performance of flight operations and aviation systems. As far as we know, some of the presented methods have not yet found an application in the aviation domain. We review applications ranging from the identification of significant operational events in air traffic operations to the prediction of potential aviation system failures for predictive maintenance.

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“…However, when false command disaggregation occurs and attackers simultaneously inject false data to confuse the security detectors [ 14 ], anomalies can not be detected. Likewise, false data estimation that used multiple data detectors with dissipativity-theoretic fault detection function in [ 14 ] and detection method based on correlations between commands and sensory data in [ 25 , 26 ] also failed in identifying the above attacks. In [ 27 ], authors used methods based on machine learning to detect attacks, however, when bad data is successively injected, the method is still ineffective to identify command disaggregation attacks.…”

Section: Related Work and Our Contributionmentioning

confidence: 99%

Command Disaggregation Attack and Mitigation in Industrial Internet of Things

Xun

Zhu

et al. 2017

Sensors

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A cyber-physical attack in the industrial Internet of Things can cause severe damage to physical system. In this paper, we focus on the command disaggregation attack, wherein attackers modify disaggregated commands by intruding command aggregators like programmable logic controllers, and then maliciously manipulate the physical process. It is necessary to investigate these attacks, analyze their impact on the physical process, and seek effective detection mechanisms. We depict two different types of command disaggregation attack modes: (1) the command sequence is disordered and (2) disaggregated sub-commands are allocated to wrong actuators. We describe three attack models to implement these modes with going undetected by existing detection methods. A novel and effective framework is provided to detect command disaggregation attacks. The framework utilizes the correlations among two-tier command sequences, including commands from the output of central controller and sub-commands from the input of actuators, to detect attacks before disruptions occur. We have designed components of the framework and explain how to mine and use these correlations to detect attacks. We present two case studies to validate different levels of impact from various attack models and the effectiveness of the detection framework. Finally, we discuss how to enhance the detection framework.

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Semi-Markov Switching Vector Autoregressive Model-Based Anomaly Detection in Aviation Systems

Cited by 45 publications

References 27 publications

Recent Advances in Anomaly Detection Methods Applied to Aviation

Recent Advances in Anomaly Detection Methods Applied to Aviation

Recent Advances in Anomaly Detection Methods applied to Aviation

Command Disaggregation Attack and Mitigation in Industrial Internet of Things

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