Skripsi

DETEKSI DAN KLASIFIKASI MALWARE PADA CITRA GRAYSCALE DENGAN MENGGUNAKAN DEEP LEARNING

---

Penilaian

---

This research focuses on the utilization of Deep Learning techniques for malware detection and classification. By representing malware samples as grayscale images, a Deep Learning model based on Convolutional Neural Networks (CNN) is developed. The model is trained using a dataset containing grayscale malware samples. Experimental results demonstrate a high level of accuracy of the Deep Learning model in detecting and classifying malware. This research contributes to the advancement of computer security systems by effectively addressing the challenges posed by malware threats using the Deep Learning approach. The research results show that the evaluation of the testing results on the trained model architecture yielded an average Accuracy of 0.96, Precision of 0.97, Recall of 0.97, and F1-Score of 0.96 using 20% of the dataset, consisting of 474 malware images.

---

Availability

Inventory Code	Barcode	Call Number	Location	Status
2307003777	T126186	T1261862023	Central Library (REFERENS)	Available but not for loan - Not for Loan

Detail Information

Series Title

-

Call Number

T1261862023

Publisher

Indralaya : Jurusan Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2023

Collation

xiv, 70 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

-

Classification

005.84 07

Content Type

-

Media Type

-

Carrier Type

-

Edition

-

Subject(s)

Jurusan Teknik Informatika
Malware

Specific Detail Info

-

Statement of Responsibility

ANUG

Other version/related

File Attachment

Comments

---

You must be logged in to post a comment