The rapid evolution of artificial intelligence (AI) in the cybersecurity domain has reshaped the way we detect and prevent
cyber threats. From log-based analysis to the early-stage detection of malware and web application attacks, AI has become
an indispensable tool. One of the most pressing challenges today is identifying malware, particularly as malicious software
like ransomware continues to wreak havoc on individuals and organizations alike.
This article focuses on malware detection using machine learning models, particularly by leveraging opcode sequences.
As a cybersecurity researcher with a strong background in reverse engineering and malware analysis, I’ve found that opcode patterns can reveal distinctive behaviors used by malware authors. In this article, I will explore how graph neural networks (GNNs) and opcode sequences work together to uncover and classify malware with a high degree of accuracy.
Static analysis helps uncover a program's malicious behavior by examining opcode sequences, byte sequences, functions,
and parameters. Commonly used methods for static malware detection include opcode frequency analysis, n-gram analysis,
and string analysis. However, each approach has its limitations. Opcode frequency-based methods are vulnerable to obfuscation techniques like dead code insertion, which significantly alters the distribution of opcodes in malware. Furthermore, these methods often focus solely on opcode frequencies, neglecting the sequential patterns that can provide deeper insights.
N-gram-based methods, while capable of capturing sequential patterns, tend to generate an excessive number of features, making them computationally expensive to implement. Similarly, string-based methods typically rely on sequence alignment algorithms, which....