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Dealing With Polymorphic and Metamorphic Malware
Abstract
The evolution of malware has introduced sophisticated threats such as polymorphic and metamorphic malware, which challenge traditional cybersecurity defenses. These malware variants evade signature-based detection by dynamically altering their code through encryption, obfuscation, and self-modifying techniques. This chapter examines the characteristics and behavior of polymorphic and metamorphic malware and highlights key detection challenges, including evasion tactics like anti-sandboxing and anti-debugging. It reviews current detection and mitigation approaches, focusing on heuristic and behavioral analysis, sandboxing, and machine learning-based methods. The study also explores emerging trends in malware detection using artificial intelligence and deep learning. With the growth of immersive digital environments, the chapter discusses metaverse security concerns, identifying new attack surfaces and potential risks to virtual platforms. The findings emphasize the need for adaptive and intelligent security mechanisms to counter evolving malware threats.
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