G-SPAMINE: An approach to discover temporal association patterns and trends in internet of things

Aljawarneh, Shadi; Radhakrishna, Vangipuram; Kumar, P. V.; Janaki, V.

doi:10.1016/j.future.2017.01.013

Cited by 152 publications

(15 citation statements)

References 35 publications

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“…Recently, similarity measures for Gaussian and z ‐space are proposed in the research literature which is pioneering work by Aljawarneh, Vangipuram, Puligadda, and Vinjamuri (2017), Aljawarneh, Radhakrishna, and Atwood (2019) Shadi Aljawarneh and Radhakrishna (2019), Radhakrishna, Aljawarneh, Kumar, and Janaki (2018), Radhakrishna, Aljawarneh, et al (2019), Radhakrishna, Veereswara Kumar, and Janaki (2018), Radhakrishna, Kumar, et al (2019), Radhakrishna, Aljawarneh, Kumar, and Choo (2018) in the area of temporal data mining. Association mining in time stamped temporal datasets is one of the most important tasks in temporal data mining.…”

Section: Literature Surveymentioning

confidence: 99%

“…Association mining in time stamped temporal datasets is one of the most important tasks in temporal data mining. An approach to determine similarity temporal patterns is discussed in Aljawarneh, Vangipuram, et al (2017) through proposing a new temporal similarity measure. Some of the similarity measures proposed to determine similarity between temporal patterns includes the contributions by Radhakrishna, Aljawarneh, Kumar, and Janaki (2018), Radhakrishna, Aljawarneh, et al (2019) Radhakrishna, Aljawarneh, Kumar, and Choo (2018).…”

Section: Literature Surveymentioning

confidence: 99%

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A machine learning approach for imputation and anomaly detection in IoT environment

Radhakrishna

Kumar

Puligadda³

et al. 2020

Expert Systems

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The problem of anomaly and attack detection in IoT environment is one of the prime challenges in the domain of internet of things that requires an immediate concern. For example, anomalies and attacks in IoT environment such as scan, malicious operation, denial of service, spying, data type probing, wrong setup, malicious control can lead to failure of an IoT system. Datasets generated in an IoT environment usually have missing values. The presence of missing values makes the classifier unsuitable for classification task. This article introduces (a) a novel imputation technique for imputation of missing data values (b) a classifier which is based on feature transformation to perform classification (c) imputation measure for similarity computation between any two instances that can also be used as similarity measure. The performance of proposed classifier is studied by using imputed datasets obtained through applying Kmeans, F-Kmeans and proposed imputation methods. Experiments are also conducted by applying existing and proposed classifiers on the imputed dataset obtained using proposed imputation technique. For experimental study in this article, we have used an open source dataset named distributed smart space orchestration system publicly available from Kaggle. Experiment results obtained are also validated using Wilcoxon non-parametric statistical test. It is proved that the performance of proposed approach is better when compared to existing classifiers when the imputation process is performed using F-Kmeans and K-Means imputation techniques. It is also observed that accuracies for attack classes scan, malicious operation, denial of service, spying, data type probing, wrong setup are 100% while it is 99% for malicious control attack class when the proposed imputation and classification technique are applied.

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Section: Literature Surveymentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

A machine learning approach for imputation and anomaly detection in IoT environment

Radhakrishna

Kumar

Puligadda³

et al. 2020

Expert Systems

Self Cite

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“…Examples of spatiotemporal data are: meteorological data [16,28], sensor data [3,31,32], network traffic [24,25], among others. An example of a spatiotemporal sensor data is{S11, W65, 0, 20150824, 0.123mm}, where the space coordinates are 11 to the south, 65 to the west, at sea level, the time coordinate is August 24, 2015, and at this place and time, it was recorded 0.123 milliliters of rain (thematic attribute).…”

Section: Theoretical Frameworkmentioning

confidence: 99%

Untitled

2020

IJDKP

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It is well known that solar energetic phenomena influence the Space Weather, in special those directed to the Earth environment. In this context, the analysis of Solar Data is a challenging task, particularly when are composed of Satellite Image Time Series (SITS). It is a multidisciplinary domain that generates a massive amount of data (several Gigabytes per year). It includes image processing, spatiotemporal characteristics, and the processing of semantic data. Aiming to enhance the SITS analysis, we propose an algorithm called "Miner

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“…Machine learning and information security techniques (Radhakrishna et al 2017;Aljawarneh et al 2016a, b;Aljawarneh et al 2007;Radhakrishna et al 2016Radhakrishna et al , 2018Aljawarneh 2017;Kalpana et al 2016;Elkobaisi et al 2015;Aljawarneh et al 2017) and approaches have been used in assessment for e-learning to improve the learning quality for higher education.…”

Section: Introduction To the Domain Of Researchmentioning

confidence: 99%

Special issue on the current trends in E-learning Assessment

2019

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In this full review paper, the recent emerging trends in E-learning Assessment have been reviewed and explored to address the recent topics and contributions in the era of Distance Education. This includes a set of rigorously reviewed world-class manuscripts addressing and detailing state-of-the-art, frameworks and techniques research projects in the area of E-learning Assessment, using different approaches such as Blockchain, Gamification, Process Mining, among others. Based on this systematic review, we have put some recommendations and suggestions for researchers, practitioners and scholars to improve their research quality in this area.

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G-SPAMINE: An approach to discover temporal association patterns and trends in internet of things

Cited by 152 publications

References 35 publications

A machine learning approach for imputation and anomaly detection in IoT environment

A machine learning approach for imputation and anomaly detection in IoT environment

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Special issue on the current trends in E-learning Assessment

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