PERANCANGAN SISTEM DETEKSI MALWARE MENGGUNAKAN METODE DEEP NEURAL NETWORK

BAGASKARA, ROMI and Nurmaini, Siti (2019) PERANCANGAN SISTEM DETEKSI MALWARE MENGGUNAKAN METODE DEEP NEURAL NETWORK. Undergraduate thesis, Sriwijaya University.

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Abstract

This research will focus on how accurately the Deep Neural Network (DNN) can detect Malware files from the dataset provided. Malware is malicious software that refers to programs that intentionally exploit vulnerabilities in computing systems for dangerous purposes. Today, with the increasing number of malware generated every day, the need for more automated and smart methods to learn, adapt and capture malware is very important, a number of advanced solutions are offered by security companies to prevent malicious malware attacks. One of them is using Machine Learning. The results of this Malware detection test showed the results of accuracy amounted to 0.9995 using the Relu and Sigmoid activation functions. Testing using another activation function also shows

Item Type: Thesis (Undergraduate)
Uncontrolled Keywords: Deep Neural Network (DNN), Malware, Machine Learning, Dataset, Activation Function , Confusion Matrix ,
Subjects: T Technology > T Technology (General) > T10.5-11.9 Communication of technical information
Divisions: 09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User: Administrator Library
Date Deposited: 26 Jul 2019 04:31
Last Modified: 02 Aug 2019 06:49
URI: http://repository.unsri.ac.id/id/eprint/897

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