IMPLEMENTASI FITUR SELEKSI PADA MALWARE DENGAN DEEP NEURAL NETWORK

IRFAN, AHMAD NAUFAL and Heryanto, Ahmad (2023) IMPLEMENTASI FITUR SELEKSI PADA MALWARE DENGAN DEEP NEURAL NETWORK. Undergraduate thesis, Sriwijaya University.

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Abstract

Malware is malicious software that refers to programs that deliberately exploit vulnerabilities in computing systems for malicious purposes, Deep Neural Network is an Artificial Neural Network with several layers between the input and output layers, Deep Neural Network has become an alternative to Machine Learning because of advances significant in the Deep Neural Network training algorithm can find the correct mathematical manipulations to convert inputs into outputs, whether it is a linearrelationship or a non-linearrelationship. The network moves through layers calculating the probability of each output. Feature selection is used to reduce information dimensions and irrelevant features or also to increase the effectiveness and efficiency capabilities of the classification algorithm. Ransomeware is malicious software that attempts to encrypt files and holds them for ransom. Users have to pay hackers to regain access to files such as images, videos or important documents. This ransomware also has several types of malware, namely Charger, Jisut, Koler, LockerPin, WannaLocker, PornDroid and others. By selecting features, the results of data visualization can reduce attributes that are not important or irrelevant in the data. Univariate selection features are selected based on high and good correlation values. It can also be seen in each classification experiment that by using the Deep neural netw

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Malware, Ransomware, Lockerpin, Deep Neural Network, feature selection
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:	Ahmad Naufal Irfan
Date Deposited:	22 Nov 2023 07:29
Last Modified:	22 Nov 2023 07:29
URI:	http://repository.unsri.ac.id/id/eprint/130817

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