Ransomware has become a major cybersecurity threat, with incidents growing in frequency and impact across critical sectors. Attackers typically deliver the malware through phishing emails, malicious downloads, or by exploiting system vulnerabilities. Traditional detection methods often struggle to keep pace with rapidly evolving ransomware strains.
The paper introduces a hybrid detection approach that combines few-shot model-agnostic meta-learning (MAML) with autoencoders. The meta-learning component allows the model to generalize from a small number of examples, which is valuable when new ransomware variants appear before large labeled datasets are available. Autoencoders are used to learn efficient feature representations, potentially improving the model's ability to distinguish malicious behavior from benign activity.
This work addresses a practical limitation of many machine-learning-based detection systems: their reliance on extensive training data. By integrating few-shot learning with representation learning, the proposed method aims to make ransomware detection more adaptable and effective in real-world settings where novel threats emerge quickly.