Anomaly Detection in Partially Observed Traffic Networks

Hou, Elizabeth; Yılmaz, Yasin; Hero, Alfred O.

doi:10.1109/tsp.2019.2892026

Cited by 19 publications

(10 citation statements)

References 46 publications

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“…Moreover, compared to most of the filters, the RLS filter has faster convergence [32]. RLS and MAP filters are being widely explored in different areas of transportation including real-time signal control [33], traffic modelling [34], traffic safety [35,36], and system identification [37].…”

Section: Methodsmentioning

confidence: 99%

Hybrid data‐driven approach for truck travel time imputation

Karimpour

Ariannezhad

2019

IET Intelligent Transport Systems

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Section: Methodsmentioning

confidence: 99%

Hybrid data‐driven approach for truck travel time imputation

Karimpour

Ariannezhad

2019

IET Intelligent Transport Systems

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“…Description Comments [79] Hierarchy of MDT models Reconstruction- [35] Discrete cosine transform [161] Non-negative matrix factorization [72] A taxonomy of domain anomalies Domain- [120] Graph kernel SVM [139] Nonlinear one-class SVM [55] Bayesian hierarchical method Probabilistic- [113] Entropy approach [157] Motion-field shape description [119] Group motion features Distance- [22] Clustering-driven deep AE scheme is shown in Fig. 4.…”

Section: Figurementioning

confidence: 99%

“…For example, Yamanaka et al [151] adopted a binary feature of auto-encoding for detection and it was a low-complexity probabilistic models. Hou et al [55] introduced the Bayesian hierarchical method to achieve detection. For probabilistic models, anomalous data can be defined as datasets that lie in low density or concentration regions of the domain of an input training distribution, such as probabilistic topic method [70] and hierarchical probabilistic model [4].…”

Section: Figurementioning

confidence: 99%

Abnormal Human Behavior Detection in Videos: A Review

Sun

Yuan

et al. 2021

ITC

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Modeling human behavior patterns for detecting the abnormal event has become an important domain in recentyears. A lot of efforts have been made for building smart video surveillance systems with the purpose ofscene analysis and making correct semantic inference from the video moving target. Current approaches havetransferred from rule-based to statistical-based methods with the need of efficient recognition of high-levelactivities. This paper presented not only an update expanding previous related researches, but also a study coveredthe behavior representation and the event modeling. Especially, we provided a new perspective for eventmodeling which divided the methods into the following subcategories: modeling normal event, predictionmodel, query model and deep hybrid model. Finally, we exhibited the available datasets and popular evaluationschemes used for abnormal behavior detection in intelligent video surveillance. More researches will promotethe development of abnormal human behavior detection, e.g. deep generative network, weakly-supervised. It isobviously encouraged and dictated by applications of supervising and monitoring in private and public space.The main purpose of this paper is to widely recognize recent available methods and represent the literature ina way of that brings key challenges into notice.

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“…Many of the anomaly‐based detection systems conventionally apply probabilistic modelling, machine learning (ML), and signal processing techniques. The authors in a previous study 11 propose a Bayesian‐based anomaly detection approach for network traffic, when the topology of the network is not known or it is too costly to observe the network directly. They model the traffic distribution using a parameterized Poisson distribution that approximates its hyperparameters leveraging the expected maximization algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

Network anomaly detection using a cross‐correlation‐based long‐range dependence analysis

et al. 2020

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The detection of anomalies in network traffic is an important task in today's Internet. Among various anomaly detection methods, the techniques based on examination of the long-range dependence (LRD) behavior of network traffic stands out to be powerful. In this paper, we reveal anomalies in aggregated network traffic by examining the LRD behavior based on the cross-correlation function of the bidirectional control and data planes traffic. Specifically, observing that the conventional cross-correlation function has a low measure of dissimilarity between the two planes, which leads to a reduced anomaly detection performance, we propose a modification of the cross-correlation function to mitigate this issue. The performance of the proposed method is analyzed using a relatively recent Internet traffic captured at King Saud University. The results demonstrate that using the modified cross-correlation function has the ability to detect low volume and short duration attacks. It also compensates for some misdetections exhibited by using the autocorrelation structures of the bidirectional traffic of the control, data, and WHOLE (combined control and data) planes traffic. 1 | INTRODUCTION Internet services have been rapidly evolving due to continuous emergence of new technologies and high bandwidth applications. According to Cisco, the annual global IP traffic is expected to reach 3.30 Zettabyte by 2021. 1 This growth has introduced many challenges in terms of securing information, reliability of data transfer, and detection of traffic anomalies and attacks. Anomalies are patterns of data that deviate from normal behavior. A broad variety of such patterns in Internet traffic are frequently faced by network operators. These patterns could represent genuine abnormalities induced by physical or technical issues, such as high-rate flows, network outage, and sudden changes due to flash crowds. 2 However, such patterns could also be generated by malicious activities such as cyber intrusions, distributed denial of service (DDoS) attacks, port scanning, and worm propagation. 3,4 This type of malicious activities could likely introduce disastrous effects on the proper operation of networks. The mentioned malicious activities are considerably growing with time. According to the worldwide infrastructure security report, 5 it is evident that the volume of these attacks on data centers, networks infrastructures, and

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Anomaly Detection in Partially Observed Traffic Networks

Cited by 19 publications

References 46 publications

Hybrid data‐driven approach for truck travel time imputation

Hybrid data‐driven approach for truck travel time imputation

Abnormal Human Behavior Detection in Videos: A Review

Network anomaly detection using a cross‐correlation‐based long‐range dependence analysis

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