Filters








14,840 Hits in 4.7 sec

A Review of anomaly detection techniques in advanced metering infrastructure

Abbas M. Al-Ghaili, Zul- Azri Ibrahim, Syazwani Arissa Shah Hairi, Fiza Abdul Rahim, Hasventhran Baskaran, Noor Afiza Mohd Ariffin, Hairoladenan Kasim
2021 Bulletin of Electrical Engineering and Informatics  
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

Osamah Ibrahim Khalaf, Kingsley A. Ogudo, S. K. B. Sangeetha, M. Praveen Kumar Reddy
2022 Mobile Information Systems  
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]

Yassine Himeur and Khalida Ghanem and Abdullah Alsalemi and Faycal Bensaali and Abbes Amira
2020 arXiv   pre-print
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

Elmer R. Magsino, Gokongwei College of Engineering, De La Salle University, Manila, Philippines
2019 International Journal of Emerging Trends in Engineering Research  
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  
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

Dr. Joy Iong Zong Chen, Dr. Smys S.
2020 Journal of Information Technology and Digital World  
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

William Hurst, Casimiro A. Curbelo Montañez, Nathan Shone
2020 IoT  
., 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

Norbik Bashah, Idris Bharanidharan Shanmugam, Abdul Manan Ahmed
2007 Zenodo  
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]

Adarsh Kumar, Krishna Gopal, Alok Aggarwal
2013 Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering  
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]

Gustavo Souto, Thomas Liebig
2016 Lecture Notes in Computer Science  
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

Guillermo Suarez-Tangil, Juan E. Tapiador, Pedro Peris-Lopez, Sergio Pastrana
2015 Pervasive and Mobile Computing  
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

Ion Bica, Bogdan-Cosmin Chifor, Ștefan-Ciprian Arseni, Ioana Matei
2019 Sensors  
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

Wenqiang Cui, Hao Wang
2017 Information  
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]

Nikola Milosevic, Junfan Huang
2019 arXiv   pre-print
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

Wenqiang Cui, Hao Wang
2017 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA)(  
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
« Previous Showing results 1 — 15 out of 14,840 results