A novel fuzzy gaussian-based dissimilarity measure for discovering similarity temporal association patterns

Radhakrishna, Vangipuram; Aljawarneh, Shadi; Kumar, P. V.; Choo, Kim‐Kwang Raymond

doi:10.1007/s00500-016-2445-y

Cited by 103 publications

(10 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%

“…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). Similarity measures Radhakrishna, Aljawarneh, Kumar, and Janaki (2018), Radhakrishna, Aljawarneh, et al (2019), Radhakrishna, Aljawarneh, Kumar, and Choo (2018) are designed to determine the similarity between patterns in Gaussian space.…”

Section: Literature Surveymentioning

confidence: 99%

“…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). Similarity measures Radhakrishna, Aljawarneh, Kumar, and Janaki (2018), Radhakrishna, Aljawarneh, et al (2019), Radhakrishna, Aljawarneh, Kumar, and Choo (2018) are designed to determine the similarity between patterns in Gaussian space. A tree‐based temporal pattern mining approach is proposed in research Radhakrishna, Kumar, Janaki, and Aljawarneh (2018), which can be applied for classification.…”

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%

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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“…Motivated from the text similarity function [8], similarity functions for measuring software component similarity (which are based on determining binary feature vectors) are proposed by Vangipuram et al [9]. Similarity measures to compute temporal similarity in Z-SPACE and gaussian space are proposed by Vangipuram et al [10]- [16] and these measures require equivalent deviation and equivalent threshold values to be determined to compute similarity in new transformation space. Another contribution is the imputation measure MANTRA [17] suggested to find similarity between complete and incomplete medical records for medical data classification.…”

Section: Background and Motivationmentioning

confidence: 99%

Similarity Based Feature Transformation for Network Anomaly Detection

Nagaraja

Boregowda²,

Khatatneh

et al. 2020

IEEE Access

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The fundamental objective behind any network intrusion detection system is to automate the detection process whenever intrusions occur in the network. The problem of the network anomaly detection is to determine, if the network incoming traffic is legitimate (or) anomalous. Automated detection systems designed to identify incoming anomalous traffic patterns usually apply widely used machine learning techniques. However, irrespective of any system model which is developed to identify anomalous traffic, all these models requires comparing anomalous and normal traffic patterns. Such comparisons implicitly depend on the ability of the underlying machine learning model to gauge the similarity between a known legitimate observation and the target. The efficiency of any network anomalous detection system depends on the use of distance (or) similarity measures and how they are actually applied. A novel distance function which can be applied to determine the similarity between two conditional feature pattern vectors is an important contribution of present research. Feature dimensionality is another important issue for any machine learning algorithm. In the present work, feature reduction is achieved using the proposed feature transformation technique. However, our approach for feature transformation uses the proposed gaussian distance function to achieve dimensionality reduction to represent the original input dataset in the new transformation space. We have also proposed new computation expressions for determining equivalent deviation and threshold in gaussian space. Experiments are performed on KDD and NSL-KDD datasets by considering widely applied classifier algorithms in various state-of-art research contributions. For performance validation of machine learning models, k-fold cross validation is applied by setting k to 10 through considering evaluation parameters such as accuracy, precision and recall. Experiment results have proved that our approach for anomaly detection that applies the proposed feature transformation technique proved comparatively better to detection methods CANN, GARUDA, and UTTAMA addressed in the recent research literature.

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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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A novel fuzzy gaussian-based dissimilarity measure for discovering similarity temporal association patterns

Cited by 103 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

Similarity Based Feature Transformation for Network Anomaly Detection

Special issue on the current trends in E-learning Assessment

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