VISUAL IMAGE-BASED MALWARE DETECTION SYSTEM USING DEEP LEARNING
The escalating strength and complexity of malware threats surpass the capacities of conventional defense systems. Traditional static analysis prevention, although highly accurate in detecting well-known malware, struggles against polymorphism and packaging evasion techniques. Dynamic analysis, on...
Saved in:
| Main Author: | |
|---|---|
| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/79488 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | The escalating strength and complexity of malware threats surpass the capacities
of conventional defense systems. Traditional static analysis prevention, although
highly accurate in detecting well-known malware, struggles against
polymorphism and packaging evasion techniques. Dynamic analysis, on the other
hand, often yields false positive results at a high frequency. A prospective avenue
in the evolution of malware detection involves visual image-based identification.
This study pioneers the integration of image-based representation and deep
learning for classification to discern the presence of malware. Not only does this
approach successfully identify malware, but it also excels in classifying malware
based on its family. The test outcomes demonstrate an impressive accuracy of
98.88% in malware detection and 94.40% in malware family classification. This
research contributes to the ongoing discourse on enhancing cybersecurity
measures by leveraging innovative techniques to combat the relentless evolution
of malicious software. As the digital landscape evolves, adopting image-based
identification methods offers a promising trajectory for robust and effective
malware detection and classification. |
|---|