Design and development of a hybrid metamorphic portable executable malware detection system
Clicks: 4
ID: 286022
2022
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Emerging Content
0.9
/100
4 views
3 readers
AI Quality Assessment
Not analyzed
Readership in this journal
EmergingRanked #1,140 of 3,757 articles by views in Malay Journal
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 3,757 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
This research paper proposes to design and develop a hybrid Metamorphic Malware Analysis of Portable Executable (PE) malware. PE files are regular executable, object codes, and Dynamic Link Libraries (DLLs) files used commonly in Windows operating systems in 32-bit and 64-bit versions. Problems, when PE malware is not detected, is its ability to install rootkits, worms, trojans, etc. Popular approaches in literatures suggest the utilization of signature-based detection. Although most studies produce high accuracy, the increasing popularity of metamorphic malware imposes a challenge in signature-based detection, as metamorphic malware has the ability to rewrite its code to appear benign. Hence, the utilization of behavioral-based detection is more useful in analyzing these types of malware. The downside of this technique is the time it takes to analyze the malware. Hence, this research proposes to design and develop a hybrid analysis system that utilizes both static and dynamic analysis to increase the overall accuracy and processing time the metamorphic PE malware detection. The signature-based detection of the malware will utilize a random forest classifier. The dynamic analysis of the system will utilize sequential learning.
| Reference Key |
persistent_1760657341_68f17fbd79477
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Diaz, Julianne Alyson I. |
| Journal | Malay Journal |
| Year | 2022 |
| DOI |
DOI not found
|
| URL | |
| Keywords | Keywords not found |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.