Ransomware Detection on Linux Operating System Using Recurrent Neural Networks with Binary Opcode Analysis

Ransomware has become a persistent and growing threat to may organizations, particularly as Linux operating systems gain prominence in enterprise and cloud environments. In this study, a novel approach is presented, leveraging Recurrent Neural Networks (RNNs) to analyze binary opcode sequences, enabling more effective detection of ransomware that evades traditional signature-based methods. By capturing the temporal dependencies within opcode sequences, the RNN model demonstrates the ability to generalize across diverse ransomware variants and accurately differentiate between malicious and benign software. Through comprehensive experimentation, the model achieved high accuracy, precision, and recall, validating its efficacy in real-time and large-scale environments. The use of opcode sequences offers computational efficiency, allowing the model to be both scalable and adaptable to new ransomware strains. The results underline the practical implications of RNN-based opcode analysis in enhancing malware detection systems, particularly for detecting advanced ransomware techniques that increasingly threaten Linux platforms.