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A Review of anomaly detection techniques in advanced metering infrastructure
2021
Bulletin of Electrical Engineering and Informatics
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Anomalies detection is a technique can be used to identify any rare event such as data manipulation that happens in AMI based on the data collected from the smart meter. ...
The purpose of this study is to review existing studies on anomalies techniques used to detect data manipulation in AMI and smart grid systems. ...
Trust
[27]
Energy fraud
detection in energy
consumption data
Irish Social
Science Data
Archive Center
WEKA
(Java)
ANNs
A: Fraud FP
~25.00%; True
negative ~75.00%
True positive
~93.75% ...
doi:10.11591/eei.v10i1.2026
fatcat:tc7cp5t4rva65jrelqbzjnqnmu
Design of Graph-Based Layered Learning-Driven Model for Anomaly Detection in Distributed Cloud IoT Network
2022
Mobile Information Systems
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To this end, we design an anomaly detection technique that attempts to efficiently monitor the entire network infrastructure to combat the spreading nature of cyber-attacks. ...
Anomaly detection systems based on graphs have been widely used to prevent network malfunction while considering the mergers of organizations involved, modeling their relationships, and integrating their ...
In this study, anomalies in emails and Twitter networks are discovered using the in-depth reading ability to learn the topological features of a social network. ...
doi:10.1155/2022/6750757
fatcat:jqp6y6g4kffnjpq6jdaevenebq
Artificial Intelligence based Anomaly Detection of Energy Consumption in Buildings: A Review, Current Trends and New Perspectives
[article]
2020
arXiv
pre-print
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If leveraged properly, that data could assist end-users, energy producers and utility companies in detecting anomalous power consumption and understanding the causes of each anomaly. ...
In this regard, this paper an in-depth review of existing anomaly detection frameworks for building energy consumption based on artificial intelligence. ...
networks [25] , suspicious behavior detection in video surveillance [26] , anomalous transaction detection in banking systems [27] and suspicious account detection in online social networks [28] . ...
arXiv:2010.04560v4
fatcat:dpullqvuv5f5lhu6tyqgdbya3q
Energy Monitoring System Incorporating Energy Profiling and Predictive Household Movement for Energy Anomaly Detection
2019
International Journal of Emerging Trends in Engineering Research
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In this work, a home energy monitoring system, EnMonSys, with predictive household movement for small and compact houses is designed and implemented to detect common energy anomaly such as the opening/ ...
Currently, EnMonSys has correctly detected 81% of energy anomaly and is believed to increase as the household profile (movement and appliance usage) is adjusted accordingly ...
EnMonSys logs individual sensor data and detects energy anomaly of an electrical equipment based on the household's standard energy consumption profile. ...
doi:10.30534/ijeter/2019/08782019
fatcat:eupejfsx7zcazlnlj67voe7cfu
An Application of Big Data in Social Media Anomaly Detection using Weight Based Technique to Compare Performance of PIG and HIVE
2019
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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In this presented work a new technique for detecting the social media anomaly profile is prepared and their implementation is described in this paper. ...
In this context the fake profiles are one of the serious problems in these days in social media. ...
Input
Figure 3 3 Figure 3: recall
Figure 4 : 4 Figure 4: memory usage
Figure 5 : 5 Figure 5: time consumption
Big Data in Social Media Anomaly Detection Using Weight Based Technique to Compare ...
doi:10.35940/ijitee.j9548.0881019
fatcat:q5xt4da3jjbo5ha4sltdbwxcem
Social Multimedia Security and Suspicious Activity Detection in SDN using Hybrid Deep Learning Technique
2020
Journal of Information Technology and Digital World
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In social multimedia, data delivery and anomaly detection services are essential in order to improve the efficiency and effectiveness of the system. ...
In social multimedia context, suspicious flow detection is performed by a hybrid deep learning based anomaly detection scheme in order to enhance the SDN reliability. ...
of instances of data proposed anomaly detection model in the next phase. ...
doi:10.36548/jitdw.2020.2.004
fatcat:satpej3hpfeplct2sed7umyg2m
Time-Pattern Profiling from Smart Meter Data to Detect Outliers in Energy Consumption
2020
IoT
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., high periods of anomalous energy consumption) within a social class grouping. ...
Capturing and mining the data from this fully maintained (and highly accurate) sensing network, provides a wealth of information for utility companies and data scientists to promote applications that can ...
Acknowledgments: The data used in this paper is available for request from the Commission for Energy Regulation (CER). ...
doi:10.3390/iot1010006
fatcat:b3o37tskyraypowyumhhzn7pvq
Hybrid Intelligent Intrusion Detection System
2007
Zenodo
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The techniques that are being investigated includes neural networks and fuzzy logic with network profiling, that uses simple data mining techniques to process the network data. ...
The proposed system is a hybrid system that combines anomaly, misuse and host based detection. ...
This result in four general groups: misuse-host, misuse-network, anomaly-host and anomaly-network. ...
doi:10.5281/zenodo.1061257
fatcat:rryehjccwfbsvckml57bsvdtay
Outlier Detection and Treatment for Lightweight Mobile Ad Hoc Networks
[chapter]
2013
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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This work is to detect and prevent unprecedented data identified from lightweight resource constraint mobile sensor devices. ...
In this work, event or error detection technique of Traag et. al., local-global outlier algorithm of Branch et. al., Teo and Tan's protocol of group key management and Cerpa et. al protocol of Frisbee ...
These anomalies can occur at data, node or network levels [10] . ...
doi:10.1007/978-3-642-37949-9_65
fatcat:eiaevxczivfvdevevafnuz6pde
On Event Detection from Spatial Time Series for Urban Traffic Applications
[chapter]
2016
Lecture Notes in Computer Science
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Besides the proliferation of smartphones and location-based social networks, also crowdsourcing and voluntary geographic data led to highly granular mobility data, maps and street networks. ...
The citizens in conjunction with their surrounding smart infrastructure turn into 'living sensors' that monitor all aspects of urban living (traffic load, noise, energy consumption, safety and many others ...
Besides, the proliferation of smartphones and location-based social networks, also crowdsourcing and voluntary geographic data led to highly granular mobility data, maps and street networks. ...
doi:10.1007/978-3-319-41706-6_11
fatcat:jk3ogwpvpjesxab3uk5ick7f5q
Power-aware anomaly detection in smartphones: An analysis of on-platform versus externalized operation
2015
Pervasive and Mobile Computing
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Many security problems in smartphones and other smart devices are approached from an anomaly detection perspective in which the main goal reduces to identifying anomalous activity patterns. ...
In this paper, we evaluate different strategies for offloading certain functional tasks in machine learning based detection systems. ...
This work was supported by the MINECO grant TIN2013-46469-R (SPINY: Security and Privacy in the Internet of You). ...
doi:10.1016/j.pmcj.2014.10.007
fatcat:37566pcw4zgihad6fywyvrmg2y
Multi-Layer IoT Security Framework for Ambient Intelligence Environments
2019
Sensors
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IoT sensors and wearables collect sensitive data and must respond in a near real-time manner to input changes. ...
The adoption of this new paradigm for health and social care largely depends on the technology deployed (sensors and wireless networks), the software used for decision-making and the security, privacy ...
Figure 3 . 3 SPN power consumption model for sensor anomaly detection.
Figure 3 . 3 SPN power consumption model for sensor anomaly detection. ...
doi:10.3390/s19184038
fatcat:2wcbql5adzhmlgbewi4sdvix5e
A New Anomaly Detection System for School Electricity Consumption Data
2017
Information
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We investigated five models within electricity consumption data from different schools to detect anomalies in the data. ...
In this paper, we focus on remote facilities management that identifies anomalous events in buildings by detecting anomalies in building electricity consumption data. ...
Acknowledgments: The authors wish to thank the domain experts of the facilities management company for the valuable inputs and evaluation of the system described in this paper. ...
doi:10.3390/info8040151
fatcat:tuzg3bsxurgbfollxnnusy5xoa
Deep learning guided Android malware and anomaly detection
[article]
2019
arXiv
pre-print
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The proposed system is able to detect and notify users about anomalies in system that is likely consequence of malware behaviour. ...
In this paper, we propose a deep learning technique that relies on LSTM and encoder-decoder neural network architectures for dynamic malware analysis based on CPU, memory and battery usage. ...
Approaches to dynamic mobile malware detection that were proposed in the past relied on detecting anomalies in battery consumption, which could be caused by malware activities (T.K. ...
arXiv:1910.10660v1
fatcat:dhcazhpwwzezxi55n7trfexadi
Anomaly detection and visualization of school electricity consumption data
2017
2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA)(
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We have investigated five models to detect anomalies in the school electricity consumption data. Furthermore, we propose a hybrid model which combines polynomial regression and Gaussian distribution. ...
Anomaly detection has been widely used in a variety of research and application domains, such as network intrusion detection, insurance/credit card fraud detection, health-care informatics, industrial ...
In this paper, semi-supervised anomaly detection is adopted to detect anomalies in school electricity consumption data, and point and collective anomalies are the target of the detection. ...
doi:10.1109/icbda.2017.8078707
fatcat:eltejiu5rra43fva4hcvk6bsha
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