EKMC: Ensemble of kNN using MetaCost for Efficient Anomaly Detection

Niranjan, A; Akshobhya, K M; Shenoy, P. Deepa; Venugopal, K R

doi:10.25046/aj040552

Cited by 3 publications

(3 citation statements)

References 7 publications

(9 reference statements)

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“…Feature Selection using SDSDR and FSF are applied to select significant features from the data set as a part of the Pre-Processing phase [9,10]. The SDSDR and FSF Feature Selection algorithms are novel and greatly reduce the dimensionality of the data set equaling to 62.33%.…”

Section: Discussionmentioning

confidence: 99%

EKMPRFG: Ensemble of KNN, Multilayer Perceptron and Random Forest using Grading for Android Malware Classification

A*.,

Abhishikth,

Shenoy

et al. 2020

IJRTE

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Android is the most popular Operating Systems with over 2.5 billion devices across the globe. The popularity of this OS has unfortunately made the devices and the services they enable, vulnerable to numerous security threats. As a result of this, a significant research is being done in the field of Android Malware Detection employing Machine Learning Algorithms. Our current work emphasizes on the possible use of Machine Learning techniques for the detection of malware on such android devices. The proposed EKMPRFG is applied for the classification of Android Malware after a preprocessing phase involving a hybrid Feature Selection model using proposed Standard Deviation of Standard Deviation of Ranks (SDSDR) and several other builtin Feature Selection algorithms such as Correlation based Feature Selection (CFS), Classifier SubsetEval, Consistency SubsetEval, and Filtered SubsetEval followed by Principal Component Analysis(PCA) for dimensionality reduction. The experimental results obtained on two data sets indicate that EKMPRFG outperforms the existing works in terms of Prediction Accuracy and Weighted F- Measure values.

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Section: Discussionmentioning

confidence: 99%

EKMPRFG: Ensemble of KNN, Multilayer Perceptron and Random Forest using Grading for Android Malware Classification

A*.,

Abhishikth,

Shenoy

et al. 2020

IJRTE

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“…Feature Selection using MMR, MSDR and FSF are applied to select significant features from the data set as a part of the Pre-Processing phase [10]. The proposed FSF returned a subset of 22 features in case of UCI Phishing data set [11] and 13 features in case of Data set1 and 11 features from Data set2. This amounts to a total reduction of features by 30% on UCI Phishing data set, 56.66% on Data set1 and 63.33% on Data set2 [12] averaging at 63.33 % of Feature Reduction without any noticeable reduction in the performance.…”

Section: Discussionmentioning

confidence: 99%

ERCRFS: Ensemble of Random Committee and Random Forest using StackingC for Phishing Classification

Niranjan¹

2020

IJETER

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The process of gathering sensitive information such as credit card/ debit card number, password, OTP etc., through a deceptive e-mail or SMS with links to Malicious site is called Phishing. An attacker during the Phishing attack masquerades as a trustworthy entity of some kind and lures the victim to provide his/her sensitive information. As per the latest data breach reports, the Phishing attacks make up almost a third of all data breaches. The attackers are using more sophisticated tools and are getting away with all the important data . Our work examines the application of Machine Learning Algorithms for the efficient classification of Phishing. The MMR, MSDMR and FSF Algorithms contribute towards the selection of significant Features from the data set such that there is no degradation in the performance of Classification. The hybrid framework ERCRFS, proposed by us ensures better Detection Accuracy values amongst all existing models.

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“…This can result in subpar effectiveness in identifying fraudulent transactions and a high rate of false positives, where fraudulent transactions are misclassified as legitimate (Ahmed and Mahmood, 2015). Datasets often tend to be imbalanced, and evaluation can be carried out using various advanced oversampling, undersampling, and hybrid-sampling methods while comparing their performance across multiple classification algorithms (Niranjan et al, 2019). Imbalanced dataset diminishes the predictive efficacy of the classifiers (Anowar and Sadaoui, 2020).…”

Section: Rq3 : What Are the Challenges Of Implementing Unsupervised L...mentioning

confidence: 99%

Financial Fraud Detection and Machine Learning Algorithm (Unsupervised Learning): Systematic Literature Review

Husnaningtyas,

Totok Dewayanto

2023

JRABA

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This research aims to assess the usage of unsupervised learning in detecting financial fraud across various financial industries by identifying cognitive constructs, benefits, economic optimization, and challenges associated with fraud detection necessitating innovative approaches for effective detection. This study conducts Systematic Literature Review following PRISMA protocol for article selection of 27 journal articles published between 2010 and 2023, sourced from Scopus database. The analysis discloses that unsupervised learning has been implemented across diverse financial sectors, including online payments, insurance, and prominently in banking, especially for identifying anomalies in credit card transactions. K-Means is the most popular method used in unsupervised learning. Nevertheless, there are ongoing challenges that require solutions to ensure the efficacy of machine learning implementation, encompassing issues like class imbalance and the complexity of fraudulent activities. In theoretical terms, this research provides an understanding of cognitive concepts, benefits and applications, challenges, and practical recommendations in the use of unsupervised learning for financial fraud detection. This is useful for practical implementation, benefiting industry practitioners in selecting appropriate models with datasets that have the potential to enhance detection system accuracy and reduce financial losses due to fraud.

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EKMC: Ensemble of kNN using MetaCost for Efficient Anomaly Detection

Cited by 3 publications

References 7 publications

EKMPRFG: Ensemble of KNN, Multilayer Perceptron and Random Forest using Grading for Android Malware Classification

EKMPRFG: Ensemble of KNN, Multilayer Perceptron and Random Forest using Grading for Android Malware Classification

ERCRFS: Ensemble of Random Committee and Random Forest using StackingC for Phishing Classification

Financial Fraud Detection and Machine Learning Algorithm (Unsupervised Learning): Systematic Literature Review

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