Data Analysis of Wireless Networks Using Classification Techniques

Canêdo, Daniel Rosa; Romariz, Alexandre Ricardo Soares

doi:10.5121/csit.2019.90905

Cited by 2 publications

References 11 publications

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Classification techniques based on Artificial immune system algorithms for Heart disease using Principal Component Analysis

Bahekar

2021

IJSRSET

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The modern era is a period of machine learning, which helps in finding new facts for future predictions. Classification is a machine learning tool that helps in the discovery of knowledge in Big data and has various potential applications. Researchers nowadays are more inclined to the techniques which are inspired by nature. The artificial immune system (AIS) is such a method that is originated by the qualities of the humanoid immune system. In this paper, artificial immune stimulated classifiers as supervised learning methods are used for classifying Heart disease datasets. The performance of the classifiers strongly depends on the datasets used for learning. Here it is observed that, when the principal component analysis is performed on the standard dataset, then classifiers' accuracy and other facts show improvement in performance, which leads to a fall in errors.

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Classification techniques based on Artificial immune system algorithms for Heart disease using Principal Component Analysis

Bahekar

2021

IJSRSET

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Network Intrusion Detection Using Linear and Ensemble ML Modeling

Parikh

Sandesara

Bhatt

2022

Advances in Wireless Technologies and Telecommunication

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Network attacks are continuously surging, and attackers keep on changing their ways in penetrating a system. A network intrusion detection system is created to monitor traffic in the network and to warn regarding the breach in security by invading foreign entities in the network. Specific experiments have been performed on the NSL-KDD dataset instead of the KDD dataset because it does not have redundant data so the output produced from classifiers will not be biased. The main types of attacks are divided into four categories: denial of service (DoS), probe attack, user to root attack (U2R), remote to local attack (R2L). Overall, this chapter proposes an intense study on linear and ensemble models such as logistic regression, stochastic gradient descent (SGD), naïve bayes, light GBM (LGBM), and XGBoost. Lastly, a stacked model is developed that is trained on the above-mentioned classifiers, and it is applied to detect intrusion in networks. From the plethora of approaches taken into consideration, the authors have found maximum accuracy (98.6%) from stacked model and XGBoost.

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Data Analysis of Wireless Networks Using Classification Techniques

Cited by 2 publications

References 11 publications

Classification techniques based on Artificial immune system algorithms for Heart disease using Principal Component Analysis

Classification techniques based on Artificial immune system algorithms for Heart disease using Principal Component Analysis

Network Intrusion Detection Using Linear and Ensemble ML Modeling

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