Mathematical Validation of Proposed Machine Learning Classifier for Heterogeneous Traffic and Anomaly Detection
The modeling of an efficient classifier is a fundamental issue in automatic training involving a large volume of representative data. Hence, automatic classification is a major task that entails the use of training methods capable of assigning classes to data objects by using the input activities pr...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2021-03-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2020.9020019 |
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| author | Azidine Guezzaz Younes Asimi Mourade Azrour Ahmed Asimi |
| author_facet | Azidine Guezzaz Younes Asimi Mourade Azrour Ahmed Asimi |
| author_sort | Azidine Guezzaz |
| collection | DOAJ |
| description | The modeling of an efficient classifier is a fundamental issue in automatic training involving a large volume of representative data. Hence, automatic classification is a major task that entails the use of training methods capable of assigning classes to data objects by using the input activities presented to learn classes. The recognition of new elements is possible based on predefined classes. Intrusion detection systems suffer from numerous vulnerabilities during analysis and classification of data activities. To overcome this problem, new analysis methods should be derived so as to implement a relevant system to monitor circulated traffic. The main objective of this study is to model and validate a heterogeneous traffic classifier capable of categorizing collected events within networks. The new model is based on a proposed machine learning algorithm that comprises an input layer, a hidden layer, and an output layer. A reliable training algorithm is proposed to optimize the weights, and a recognition algorithm is used to validate the model. Preprocessing is applied to the collected traffic prior to the analysis step. This work aims to describe the mathematical validation of a new machine learning classifier for heterogeneous traffic and anomaly detection. |
| format | Article |
| id | doaj-art-d92fccf6030041e4a6b867d7655eac20 |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2021-03-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-d92fccf6030041e4a6b867d7655eac202025-02-02T06:50:16ZengTsinghua University PressBig Data Mining and Analytics2096-06542021-03-0141182410.26599/BDMA.2020.9020019Mathematical Validation of Proposed Machine Learning Classifier for Heterogeneous Traffic and Anomaly DetectionAzidine Guezzaz0Younes Asimi1Mourade Azrour2Ahmed Asimi3<institution content-type="dept">Department of Computer Science and Mathematics, High School of Technology</institution>, <institution>Cadi Ayyad University</institution>, <city>Essaouira</city> <postal-code>44000</postal-code>, <country>Morocco</country>.<institution content-type="dept">Department of Computer Science</institution>, <institution>High School of Technology, Ibn Zohr University</institution>, <city>Guelmim</city> <postal-code>81000</postal-code>, <country>Morocco</country>.<institution content-type="dept">IDMS Team, Department of Computer Science, Faculty of Science and Technology</institution>, <institution>Moulay Ismail University</institution>, <city>Errachidia</city> <postal-code>52000</postal-code>, <country>Morocco</country>.<institution content-type="dept">Department of Computer Science and Mathematics, Faculty of Sciences Agadir</institution>, <institution>Ibn Zohr University</institution>, <city>Agadir</city> <postal-code>80000</postal-code>, <country>Morocco</country>.The modeling of an efficient classifier is a fundamental issue in automatic training involving a large volume of representative data. Hence, automatic classification is a major task that entails the use of training methods capable of assigning classes to data objects by using the input activities presented to learn classes. The recognition of new elements is possible based on predefined classes. Intrusion detection systems suffer from numerous vulnerabilities during analysis and classification of data activities. To overcome this problem, new analysis methods should be derived so as to implement a relevant system to monitor circulated traffic. The main objective of this study is to model and validate a heterogeneous traffic classifier capable of categorizing collected events within networks. The new model is based on a proposed machine learning algorithm that comprises an input layer, a hidden layer, and an output layer. A reliable training algorithm is proposed to optimize the weights, and a recognition algorithm is used to validate the model. Preprocessing is applied to the collected traffic prior to the analysis step. This work aims to describe the mathematical validation of a new machine learning classifier for heterogeneous traffic and anomaly detection.https://www.sciopen.com/article/10.26599/BDMA.2020.9020019anomaly detectionheterogeneous trafficpreprocessingmachine learningtrainingclassification |
| spellingShingle | Azidine Guezzaz Younes Asimi Mourade Azrour Ahmed Asimi Mathematical Validation of Proposed Machine Learning Classifier for Heterogeneous Traffic and Anomaly Detection Big Data Mining and Analytics anomaly detection heterogeneous traffic preprocessing machine learning training classification |
| title | Mathematical Validation of Proposed Machine Learning Classifier for Heterogeneous Traffic and Anomaly Detection |
| title_full | Mathematical Validation of Proposed Machine Learning Classifier for Heterogeneous Traffic and Anomaly Detection |
| title_fullStr | Mathematical Validation of Proposed Machine Learning Classifier for Heterogeneous Traffic and Anomaly Detection |
| title_full_unstemmed | Mathematical Validation of Proposed Machine Learning Classifier for Heterogeneous Traffic and Anomaly Detection |
| title_short | Mathematical Validation of Proposed Machine Learning Classifier for Heterogeneous Traffic and Anomaly Detection |
| title_sort | mathematical validation of proposed machine learning classifier for heterogeneous traffic and anomaly detection |
| topic | anomaly detection heterogeneous traffic preprocessing machine learning training classification |
| url | https://www.sciopen.com/article/10.26599/BDMA.2020.9020019 |
| work_keys_str_mv | AT azidineguezzaz mathematicalvalidationofproposedmachinelearningclassifierforheterogeneoustrafficandanomalydetection AT younesasimi mathematicalvalidationofproposedmachinelearningclassifierforheterogeneoustrafficandanomalydetection AT mouradeazrour mathematicalvalidationofproposedmachinelearningclassifierforheterogeneoustrafficandanomalydetection AT ahmedasimi mathematicalvalidationofproposedmachinelearningclassifierforheterogeneoustrafficandanomalydetection |