ERCR TV: Ensemble of Random Committee and Random Tree for Efficient Anomaly Classification Using Voting

Niranjan, A; Nutan, D H; Nitish, A; Shenoy, P. Deepa; Venugopal, K R

doi:10.1109/i2ct.2018.8529797

Cited by 20 publications

(13 citation statements)

References 6 publications

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“…The proposed EKMC performs better than our previous model i.e. ERCRTV [1] and the existing GAA-ADS [8] models when tested on both the data sets. EKMC exhibits good Prediction Accuracy and a better Detection Rate as listed in Table 5 and depicted in Figure 2.…”

Section: Resultsmentioning

confidence: 80%

“…In this extension work [1], we propose SDMR for Feature Selection that exploits the advantages of various existing Weight Based Ranking Algorithms. In addition to SDMR, the data set is also subjected to PCA for dimensionality reduction during the preprocessing stage.…”

Section: Astesj Issn: 2415-6698mentioning

confidence: 99%

“…ERCRTV [1] that forms the base work for the current work, uses Correlation based Feature Selection (CFS) algorithm for Feature Selection from the NSL KDD and KDD CUP 99 data sets. It selects only eight prominent features from them.…”

Section: Background and Related Workmentioning

confidence: 99%

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EKMC: Ensemble of kNN using MetaCost for Efficient Anomaly Detection

Niranjan¹,

Akshobhya²,

Shenoy³

et al. 2019

Adv. sci. technol. eng. syst. j.

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Anomaly detection aims at identification of suspicious items, observations or events by differing from most of the data. Intrusion Detection, Fault Detection, and Fraud Detection are some of the various applications of Anomaly Detection. The Machine learning classifier algorithms used in these applications would greatly affect the overall efficiency. This work is an extension of our previous work ERCRTV: Ensemble of Random Committee and Random Tree for Efficient Anomaly Classification using Voting. In the current work, we propose SDMR a simple Feature Selection Technique to select significant features from the data set. Furthermore, to reduce the dimensionality, we use PCA in the pre-processing stage. The EKMC (Ensemble of kNN using MetaCost) with tenfold cross validation is then applied on the pre-processed data. The performance of EKMC is evaluated on UNSW_NB15 and NSL KDD data sets. The results of EKMC indicate better detection rate and prediction accuracy with a lesser error rate than other existing methods.

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Section: Resultsmentioning

confidence: 80%

Section: Astesj Issn: 2415-6698mentioning

confidence: 99%

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EKMC: Ensemble of kNN using MetaCost for Efficient Anomaly Detection

Niranjan¹,

Akshobhya²,

Shenoy³

et al. 2019

Adv. sci. technol. eng. syst. j.

Self Cite

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show abstract

“…Initially, The suggested form has been taught and tested using the train set and test set respectively. A tenfold cross-validation was conducted to evaluate performance metrics [4]. The information is either labeled as normal or one of 38 distinct kinds of attacks in the NSL-KDD dataset.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…The rank algorithm for the filter method is used to keep the characteristics from higher priority to least. Compressive classification algorithms are numerous [4], Each with its own strengths and weaknesses. There is no single best-performing learning algorithm, but we selected five of them (innocent Buyers, Random Tree, Random Committee, Decision Table, Random Forest) in our analysis, and will try to find out which algorithm is best suited to the dataset [5].…”

Section: Methodsmentioning

confidence: 99%

Multilevel Fraud Detection System using Voting Techniques

Agrawal*¹,

Parmar²

2019

IJITEE

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Fraud detection is an enduring topic that pose a threat to the information security systems. Data mining helps detect and rapidly identify fraud and take instant action to reduce losses. It analysis the various forms of attacks through classifications algorithms of data mining. In this dissertation the data set of NSL-KDD is examined in identifying anomalies in network traffic patterns and the importance of various classification techniques is studied. Firstly the data is classify in two classes Normal and Anomaly and after then the anomaly data categories in various Attack forms i.e Probe, U2R, R2L, DOS to detect the fraud. The analysis performed through feature selection and classification techniques present in WEKA tool of data mining. The features are extracted through feature selection (CfsSubsetEval, InfoGain, FilterSubsetEval and FilterAttributeEval) methods from 41 attributes to 11 attributes using CfsSubsetEval with best first Search and after extraction the features the classification algorithms(Naïve Bayes, RC, RT, RF and DT) applied for finding Accuracy. The best Result in terms of Accuracy is finding through the voting method using Random Committee and Random Forest is 99.94%.

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Building Classes for Sentiment of Social Media Content Using WU Semantic Measure

Kayed

Saidi

2022

Advances in Intelligent Systems and Computing

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ERCR TV: Ensemble of Random Committee and Random Tree for Efficient Anomaly Classification Using Voting

Cited by 20 publications

References 6 publications

EKMC: Ensemble of kNN using MetaCost for Efficient Anomaly Detection

EKMC: Ensemble of kNN using MetaCost for Efficient Anomaly Detection

Multilevel Fraud Detection System using Voting Techniques

Building Classes for Sentiment of Social Media Content Using WU Semantic Measure

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