OPTIMALISASI PERFORMA ALGORITMA CONVOLUTIONAL NEURAL NETWORK (CNN) DALAM PROSES KLASIFIKASI MALWARE BOTNET PADA JARINGAN INTERNET OF THINGS

RATFIANA, SONIAWATI and Heryanto, Ahmad (2023) OPTIMALISASI PERFORMA ALGORITMA CONVOLUTIONAL NEURAL NETWORK (CNN) DALAM PROSES KLASIFIKASI MALWARE BOTNET PADA JARINGAN INTERNET OF THINGS. Undergraduate thesis, Sriwijaya University.

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Abstract

One of the threats to the internet network is botnet (robot network). although there are many methods used to detect botnet, but there are still less accurate. this can be seen from the results of accuracy, precision and others that differ greatly due to imbalanced datasets. This research examines the classification of botnet attacks and builds the best and accurate CNN model by optimizing the CICIDS-17 dataset consisting of 97718 BENIGN data and 128027 DDoS data by applying undersampling techniques and AlexNet and LeNet architectures in the Convolutional Neural Network method. After being optimized, AlexNet architecture gets an accuracy of 99.97% from 99.94% and the loss value decreases from 0.49% to 0.11%. while Lenet architecture the accuracy increases from 99.88% to 99.93% and the loss value decreases from 0.40% to 0.24%. Based on the accuracy graph, both models are neither overfitting nor underfitting. Meanwhile, based on the confusion matrix, it can be seen that the model is able to classify botnets quite well. Keywords : Convolutional Neural Network, CICIDS-17, Botnet IoT, AlexNet, LeNet

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Convolutional Neural Network, CICIDS-17, Botnet IoT, AlexNet, LeNet
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Soniawati Ratfiana
Date Deposited:	18 Oct 2023 03:26
Last Modified:	18 Oct 2023 03:26
URI:	http://repository.unsri.ac.id/id/eprint/129898

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