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Ensemble learning approach to enhancing binary classification in Intrusion Detection System for Internet of Thing

100%

Soni , Remli M. A. , Daud K. , Amien J. A.

International Journal of Electronics and Telecommunications

2024

tom Vol. 70, No. 2

465--472

The Internet of Things (IoT) has experienced significant growth and plays a crucial role in daily activities. However, along with its development, IoT is very vulnerable to attacks and raises concerns for users. The Intrusion Detection System (IDS) operates efficiently to detect and identify suspicious activities within the network. The primary source of attacks originates from external sources, specifically from the internet attempting to transmit data to the host network. IDS can identify unknown attacks from network traffic and has become one of the most effective network security. Classification is used to distinguish between normal class and attacks in binary classification problem. As a result, there is a rise in the false positive rates and a decrease in the detection accuracy during the model's training. Based on the test results using the ensemble technique with the ensemble learning XGBoost and LightGBM algorithm, it can be concluded that both binary classification problems can be solved. The results using these ensemble learning algorithms on the ToN IoT Dataset, where binary classification has been performed by combining multiple devices into one, have demonstrated improved accuracy. Moreover, this ensemble approach ensures a more even distribution of accuracy across each device, surpassing the findings of previous research.

Multiple SVMs Modelling Method for Fault Diagnosis of Power Transformers

75%

Zhang M. , Li K. , Tian H.

Przegląd Elektrotechniczny

2012

tom R. 88, nr 7b

232--234

For enhancing the accuracy of fault diagnosis for power transformers, a multiple SVMs scheme is proposed in this paper. In this scheme, SVM is used to establish the base classifier for its good performance and fast learning speed. Secondly, the several base classifiers based on single SVM will be combined by consulting ensemble techniques. And then a multiple SVM s method is obtained. The real gas records data from a power company is used to establish fault diagnosis system for power transformers based on the new multiple SVM s method. For comparison, the conventional methods are used to build fault diagnosis models by the same data. The experiments demonstrate the new multiple SVMs method has the best performance in both learning ability aspect and generalization ability aspect for fault diagnosis of power transformers.

Zaproponowano schemat SVM (support vector machine) w celu poprawy dokładności diagnostyki transformatorów mocy. W pprównaniu do metod konwencjonalnych proponowana metoda ma możliwość uczenia się i efektywnego wykorzystania bazy danych.