SELF-SUPERVISED LEARNING TO DETECT GENERAL ADVERSARIAL NETWORK MALWARE

• Main Author: Rananta Natasha, Dita
• Format: Theses
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/81670
• Institution: Institut Teknologi Bandung

Cybersecurity threats are increasing with the development of malware types and variations. Effective malware detection is critical to maintaining data integrity and security. Traditionally, malware detection methods rely on definition-based signatures that are inefficient against evolving malware...