Muhammad Hazriq Akmal, Zairol (2023) Malware Detection In Android Using Machine Learning. Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah.
|
Pdf
CA20144.pdf - Accepted Version Download (1MB) | Preview |
Abstract
In an era that is increasingly fast with advanced technology, smartphones are a priority and a necessity for everyone. These gadgets are developing every day towards more advanced and appropriate ways of use. However, security is one of the causes of concern for many smartphone users. Safety is an important aspect that is highly regarded and taken seriously by some parties, and if this safety issue is taken for granted and not taken care of, it will cause problems to the people surrounding. Just like the security issue of smartphone users, which is now increasingly prevalent with one of the biggest threats to all gadgets, which is the malware issue. Studies have shown that there is an increase from year to year regarding malware that is more focused on attacking and damaging the victim's smartphone, especially for Android users. Many Android users have been affected by this malware problem and various solutions have been implemented. This study aims to examine the ways and methods of detecting malware that has attacked the Android operating system, and suggest the detection of a malware detection system by using machine learning techniques. The results show that machine learning is a more promising approach with 90% accuracy in experiments that have been conducted for machine learning methods for higher malware detection and prove that this malware detection system can detect Android malware more efficiently.
| Item Type: | Undergraduates Project Papers |
|---|---|
| Additional Information: | SV: Ts. Dr. Mohd Faizal bin Ab Razak |
| Uncontrolled Keywords: | smartphone security |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Faculty/Division: | Faculty of Computing |
| Depositing User: | Mr. Nik Ahmad Nasyrun Nik Abd Malik |
| Date Deposited: | 18 Mar 2024 08:04 |
| Last Modified: | 18 Mar 2024 08:04 |
| URI: | http://umpir.ump.edu.my/id/eprint/40708 |
| Download Statistic: | View Download Statistics |
Actions (login required)
 |
View Item |