Scalable Anomaly Detection for Smart City Infrastructure Networks

Difallah, Djellel Eddine; Cudré-Mauroux, Philippe; McKenna, Sean Andrew

doi:10.1109/mic.2013.84

Cited by 41 publications

(23 citation statements)

References 5 publications

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“…It also enables APIs for data access in eXtensible Markup Language (XML) and JavaScript Object Notation (JSON) formats according to different sensing sites or sensing species. Due to the provenance of the sensed data and known static context, fixed sensing has been applied for numerous sensing applications, such as air quality sensing [32,65], weather conditions [72], water distribution data [71] and traffic data [56,72]. Different supporting frameworks are also proposed [18,40,56].…”

Section: Physical Sensor Deploymentsmentioning

confidence: 99%

“…Mobile sensing gives an opportunity for large scale environmental monitoring with limited number of sensors, however, it also needs a proper approach for managing the fast changes in the locations of data and data sources [62]. sensors involved in the water distribution network system [71] include the water sensors, which monitor the flow and pressure, and water quality sensors. The fixed sensor installation in the noise mapping architecture in [18] is based on the SmartSantander IoT architecture and is a realization of a WSN for urban sensing, with sensors mounted on public properties such as on street and traffic lights and on building facades.…”

Section: Physical Sensor Deploymentsmentioning

confidence: 99%

“…A spatiotemporal anomaly detection algorithm is proposed in [71] for the data stream obtained from the sensors in a water distribution network. The authors propose two extensions to the Local Indicators of Spatial Association (LISA) metric [96] for anomaly detection, involving extending the local neighborhood to contain both temporal (past measurements) and spatial factors (neighbor node measurements) and limiting the mean and sigma calculations to the local base station node measurements.…”

Section: Event Detectionmentioning

confidence: 99%

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Cyber–Physical–Social Frameworks for Urban Big Data Systems: A Survey

Zhou

Abad³

et al. 2017

Applied Sciences

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Abstract:The integration of things' data on the Web and Web linking for things' description and discovery is leading the way towards smart Cyber-Physical Systems (CPS). The data generated in CPS represents observations gathered by sensor devices about the ambient environment that can be manipulated by computational processes of the cyber world. Alongside this, the growing use of social networks offers near real-time citizen sensing capabilities as a complementary information source. The resulting Cyber-Physical-Social System (CPSS) can help to understand the real world and provide proactive services to users. The nature of CPSS data brings new requirements and challenges to different stages of data manipulation, including identification of data sources, processing and fusion of different types and scales of data. To gain an understanding of the existing methods and techniques which can be useful for a data-oriented CPSS implementation, this paper presents a survey of the existing research and commercial solutions. We define a conceptual framework for a data-oriented CPSS and detail the various solutions for building human-machine intelligence.

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Section: Physical Sensor Deploymentsmentioning

confidence: 99%

Section: Physical Sensor Deploymentsmentioning

confidence: 99%

Section: Event Detectionmentioning

confidence: 99%

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Cyber–Physical–Social Frameworks for Urban Big Data Systems: A Survey

Zhou

Abad³

et al. 2017

Applied Sciences

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“…Difallah et al [26] propose a scalable ADS for Water Distribution Networks. Specifically, they use Local Indicators of Spatial Association (LISA) [108] as a metric for anomaly detection, by extending the metric to consider temporal associations.…”

Section: Physical-level Adssmentioning

confidence: 99%

Towards Large-Scale, Heterogeneous Anomaly Detection Systems in Industrial Networks: A Survey of Current Trends

Iturbe

Garitano

Zurutuza

et al. 2017

Security and Communication Networks

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Industrial Networks (INs) are widespread environments where heterogeneous devices collaborate to control and monitor physical processes. Some of the controlled processes belong to Critical Infrastructures (CIs), and, as such, IN protection is an active research field. Among different types of security solutions, IN Anomaly Detection Systems (ADSs) have received wide attention from the scientific community. While INs have grown in size and in complexity, requiring the development of novel, Big Data solutions for data processing, IN ADSs have not evolved at the same pace. In parallel, the development of Big Data frameworks such as Hadoop or Spark has led the way for applying Big Data Analytics to the field of cyber-security, mainly focusing on the Information Technology (IT) domain. However, due to the particularities of INs, it is not feasible to directly apply IT security mechanisms in INs, as IN ADSs face unique characteristics. In this work we introduce three main contributions. First, we survey the area of Big Data ADSs that could be applicable to INs and compare the surveyed works. Second, we develop a novel taxonomy to classify existing IN-based ADSs. And, finally, we present a discussion of open problems in the field of Big Data ADSs for INs that can lead to further development.

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“…Experiments with large-scale sensor networks that enable real-time monitoring of critical infrastructure have included work on the urban water supply [6] and defining key interfaces to buildings to allow smartgrid managers to interactively manage energy use for the city [12].…”

Section: Background and Related Workmentioning

confidence: 99%

City Hub: A Cloud-Based IoT Platform for Smart Cities

Lea

Blackstock

2014

2014 IEEE 6th International Conference on Cloud Computing Technology and Science

114

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Cloud based Smart City hubs are an attractive approach to addressing some of the complex issues faced when deploying PaaS infrastructure for Smart Cities. In this paper we introduce the general notion of IoT hubs and then discuss our work to generalize our IoT hub as a Smart City PaaS. We briefly describe our approach and discuss our experiences deploying two cloud-based Smart City hubs, one in the UK and the other in Canada.

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Scalable Anomaly Detection for Smart City Infrastructure Networks

Cited by 41 publications

References 5 publications

Cyber–Physical–Social Frameworks for Urban Big Data Systems: A Survey

Cyber–Physical–Social Frameworks for Urban Big Data Systems: A Survey

Towards Large-Scale, Heterogeneous Anomaly Detection Systems in Industrial Networks: A Survey of Current Trends

City Hub: A Cloud-Based IoT Platform for Smart Cities

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