A detection model for anomalies in smart grid with sensor network

Kher, Shubhalaxmi; Nutt, Victor; Dasgupta, Dipankar; Ali, Hasan; Mixon, Paul

doi:10.1109/fiiw.2012.6378345

Cited by 11 publications

(8 citation statements)

References 5 publications

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“…The main goal of this research is to detect anomalies using machine learning technique in sensor network smart grid, Sensor data was collected from multiple sensors, Data analyzed using decision tree J48 to detect anomalies. A result shows that decision tree J48 is the highest classification rate compared with other machine learning techniques [10].…”

Section: Related Workmentioning

confidence: 90%

Anomaly Detection using Hadoop and MapReduce Technique in Cloud with Sensor Data

Alghussein¹,

Aly²,

El-Nasr³

2015

IJCA

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This paper presents a model to observation the Cloud computing for any anomalous activity. Hadoop it is a largely used open source Cloud Computing framework to huge data. It uses the model Machine Learning technique to detect classify anomalies of sensory observation and help to in ensuring the stabilization of virtual sensor networks. The framework it's built on top of the Hadoop and MapReduce implementation which is use one of the Machines Learning techniques to detect these anomalies. Preliminary results show that our classification mechanism is promising and able to detect anomalous events that may cause a threat to the Cloud Computing.

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Section: Related Workmentioning

confidence: 90%

Anomaly Detection using Hadoop and MapReduce Technique in Cloud with Sensor Data

Alghussein¹,

Aly²,

El-Nasr³

2015

IJCA

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“…For wireless adapters, the basic actions such as SLEEP, RECEIVE and AWAKE follow a regular sequence in normal cases. Kher [40] proposed a model for monitoring the smart grid against malicious activities or attacks using machine learning techniques.…”

Section: A Cyber Defensementioning

confidence: 99%

A survey on bad data injection attack in smart grid

Wang

Guan

Liu

et al. 2013

2013 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)

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Bad data injection (BDI) is one of the most threatening attacks in smart grid, as it may cause energy theft of end users, false dispatch on the distribution process, and device breakdown during power generation. In this paper, the BDI attack is defined as a cyber-physical attack which is a combination of two aspects: 1) on the cyber side, modern attack techniques are exploited to intrude and inject bad data into the information system; 2) on the physical side, attackers construct the bad data to bypass the traditional error detection in power systems. Related work on BDI construction and implementation are reviewed. An attack simulation is constructed to illustrate how to launch a BDI attack. The countermeasures against the BDI are also summarized from the views of cyber-orientation, physical-orientation. Finally, our work on cyber-physical fusion detection is presented.Index Terms--smart grid; security; bad data injection; cyberphysical fusion.

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“…Kher et.al.in [5] [6] proposed a sensor network-based hybrid framework for SG monitoring. The two-layer mesh topology routing protocol consists of local clusters with fixed number of heads at lower level and cluster heads at each node form a linear chain at the upper level.…”

Section: Literature Survey On Machine Learning (Ml) Approaches For Anomaly Detection In Smart Gridmentioning

confidence: 99%

An Introductory Review Of Anomaly Detection Methods In Smart Grids

Preethi¹,

K²

2021

Proceedings of the First International Conference on Combinatorial and Optimization, ICCAP 2021, December 7-8 2021, Chennai, In

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Cyber Physical systems such as smart grids have the potential to address the future energy crisis. Because of the bidirectional flow of information across various domains in a smart grid, anomaly detection is one of the prime security related challenges. Machine learning models have emerged as one of the prospective artificial intelligence technologies to model supervised and unsupervised data for analysis and prediction. This paper reviews the various anomaly detection schemes in a Smart Grid Infrastructure based on machine learning techniques.

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A detection model for anomalies in smart grid with sensor network

Cited by 11 publications

References 5 publications

Anomaly Detection using Hadoop and MapReduce Technique in Cloud with Sensor Data

Anomaly Detection using Hadoop and MapReduce Technique in Cloud with Sensor Data

A survey on bad data injection attack in smart grid

An Introductory Review Of Anomaly Detection Methods In Smart Grids

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