What else to take into account

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Er. Ms. Kritika
Independent Researcher
Mon, 12/09/2024 - 00:01

To combat ransomware effectively, a multi-faceted approach is needed, including the development of models that minimize feature sets to improve efficiency and accuracy, while ensuring compatibility across platforms like Linux and macOS. Key challenges like data scarcity, imbalanced datasets, and concept drift can be addressed through techniques like transfer learning, few-shot learning, and data augmentation.

Hybrid models integrating AI, IoT, and deep learning are essential for building robust, early detection systems. Innovations such as ransomware sandboxing, AI-based predictive analysis, enhanced data tokenization, and automated response kits can further strengthen defenses. Additionally, decentralized identity management (via blockchain) and User Behavior Analytics (UBA) tools can improve security.

Future research should focus on enhancing model interpretability through Explainable AI (XAI), real-time, resource-efficient detection systems for IoT environments, and expanding detection techniques to domains like cloud computing. Incorporating quantum-resistant encryption, secure programming languages like Rust, and integrating machine learning with broader cybersecurity frameworks are critical for protecting against emerging threats. Collaboration between academia, industry, and law enforcement will be key to keeping up with evolving ransomware tactics.

Full paper link: https://www.sciencedirect.com/science/article/pii/S2772918424000444?via…

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