Empirical analysis of ensemble methods for the classification of robocalls in telecommunications

Ghosh, Meghna; Prabu, P.

doi:10.11591/ijece.v9i4.pp3108-3114

Cited by 15 publications

(4 citation statements)

References 5 publications

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“…Different machine learning approaches have been used to detect SPIT attacks, e.g., the deep learning approach [21,22]. In [23,24], a new supervised learning-based technique was developed for detecting different sources of bots despite their botnet characteristics in a real-world, highly imbalanced network dataset.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, it was highlighted that the existing techniques can only detect human spam calls and not robocalls in view of the aforementioned approaches. A machine learning approach to spam detection has been proposed as one solution to this problem, e.g., the Ensemble Approach [22]. Previously proposed machine learning-based malicious call detection methods relied on various assumptions that a telephony network server could provide more information about the caller.…”

Section: Related Workmentioning

confidence: 99%

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Performance Evaluation of Machine Learning based Robocalls Detection Models in Telephony Networks

Akinyemi¹,

Odukoya²,

Sanni³

et al. 2022

IJCNIS

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Many techniques have been proposed to detect and prevent spam over Internet telephony. Human spam calls can be detected more accurately with these techniques. However, robocalls, a type of voice spammer whose calling patterns are similar to those of legitimate users, cannot be detected as effectively. This paper proposes a model for robocall detection using a machine learning approach. Voice data recordings were collected and the relevant features for study were selected. The selected features were then used to formulate six (6) detection models. The formulated models were simulated and evaluated using some performance metrics to ascertain the model with the best performance. The C4.5 decision tree algorithm gave the best evaluation result with an accuracy of 99.15%, a sensitivity of 0.991%, a false alarm rate of 0.009%, and a precision of 0.992%. As a result, it was concluded that this approach can be used to detect and filter both machine-initiated and human-initiated spam calls.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Performance Evaluation of Machine Learning based Robocalls Detection Models in Telephony Networks

Akinyemi¹,

Odukoya²,

Sanni³

et al. 2022

IJCNIS

View full text Add to dashboard Cite

show abstract

“…The major challenges and opportunities with the imbalanced data set were clearly given in [15]. Ensemble learning is effectively implemented on classification problems [16,17]. Bukhtoyarov et al [18] have developed ensemble based on Genetic Programming known as (GPEN) to categorize the input intrusions as Probe or non-Probe attacks, with nine of the 41 features.…”

Section: Related Workmentioning

confidence: 99%

A novel ensemble modeling for intrusion detection system

Priyadarsini¹,

Anuradha²

2020

IJECE

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Vast increase in data through internet services has made computer systems more vulnerable and difficult to protect from malicious attacks. Intrusion detection systems (IDSs) must be more potent in monitoring intrusions. Therefore an effectual Intrusion Detection system architecture is built which employs a facile classification model and generates low false alarm rates and high accuracy. Noticeably, IDS endure enormous amounts of data traffic that contain redundant and irrelevant features, which affect the performance of the IDS negatively. Despite good feature selection approaches leads to a reduction of unrelated and redundant features and attain better classification accuracy in IDS. This paper proposes a novel ensemble model for IDS based on two algorithms Fuzzy Ensemble Feature selection (FEFS) and Fusion of Multiple Classifier (FMC). FEFS is a unification of five feature scores. These scores are obtained by using feature-class distance functions. Aggregation is done using fuzzy union operation. On the other hand, the FMC is the fusion of three classifiers. It works based on Ensemble decisive function. Experiments were made on KDD cup 99 data set have shown that our proposed system works superior to well-known methods such as Support Vector Machines (SVMs), K-Nearest Neighbor (KNN) and Artificial Neural Networks (ANNs). Our examinations ensured clearly the prominence of using ensemble methodology for modeling IDSs. And hence our system is robust and efficient.

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“…If 4873 a training set, the model might not be appropriate for some data, for which another model can produce better results [2]. Ensemble classifiers are among the multicomponent classifiers defined to produce better results than a single-component classifier [7]. In such classifications, ensemble classifiers are used to obtain better results.…”

Section: Introductionmentioning

confidence: 99%

A new approach to image classification based on a deep multiclass AdaBoosting ensemble

Asil

Bagherzadeh

2020

IJECE

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In recent years, deep learning methods have been developed in order to solve the problems. These methods were effective in solving complex problems. Convolution is one of the learning methods. This method is applied in classifying and processing of images as well. Hybrid methods are another multi-component machine learning method. These methods are categorized into independent and dependent types. Ada-Boosting algorithm is one of these methods. Today, the classification of images has many applications. So far, several algorithms have been presented for binary and multi-class classification. Most of the above-mentioned methods have a high dependence on the data. The present study intends to use a combination of deep learning methods and associated hybrid methods to classify the images. It is presumed that this method is able to reduce the error rate in images classification. The proposed algorithm consists of the Ada-Boosting hybrid method and bi-layer convolutional learning method. The proposed method was analyzed after it was implemented on a multi-class Mnist data set and displayed the result of the error rate reduction. The results of this study indicate that the error rate of the proposed method is less than Ada-Boosting and convolution methods. Also, the network has more stability compared to the other methods.

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Empirical analysis of ensemble methods for the classification of robocalls in telecommunications

Cited by 15 publications

References 5 publications

Performance Evaluation of Machine Learning based Robocalls Detection Models in Telephony Networks

Performance Evaluation of Machine Learning based Robocalls Detection Models in Telephony Networks

A novel ensemble modeling for intrusion detection system

A new approach to image classification based on a deep multiclass AdaBoosting ensemble

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