Quantum-Lattice Feature Extraction for Ransomware Detection Using Multi-Dimensional Cryptographic Signatures
Oliver Anka, Annabelle Clarke, Benjamin Dixon, James Hall · 2024
The escalating sophistication of cyber threats necessitates innovative detection methodologies to safeguard digital infrastructures. Traditional ransomware detection techniques often falter against novel and obfuscated attacks, highlighting the need for more adaptive solutions. The Dynamic Quantum-Lattice Feature Extraction (DQLE) methodology offers a transformative approach, leveraging quantum-lattice structures to capture intricate patterns inherent in ransomware behavior. By integrating DQLE into detection pipelines, the system achieves high accuracy and low false positive rates across diverse ransomware variants, demonstrating its efficacy in real-time applications. Comprehensive evaluations reveal the system's robustness against common evasion techniques, such as code obfuscation and polymorphism, showing its adaptability to evolving cyber threats. However, the computational demands associated with quantum-lattice processing present challenges for deployment in resource-constrained environments. Despite these limitations, the DQLE framework represents a significant advancement in cybersecurity, offering a promising foundation for future developments in ransomware detection.