Ransomware Detection Using Quantum Neural Networks

Ransomware is a malicious software (malware) that encodes the files on a victim’s computer or network, which makes them inaccessible. Once the files are encoded, the intruder typically claims a ransom, often in cryptocurrency, in return for the decoding key that gives access to the files. Preventing ransomware attacks usually involves a combination of security protocols, including regular data backups, user education, and the use of antivirus software. An innovative approach is proposed for the ransomware detection using Quantum Neural Networks (QNNs), harnessing the guidelines of quantum computing to amplify detection precision and optimization of the network. This technique involves training a QNN on labeled datasets of ransomware and benign software, focusing on detecting anomalies in file behavior, encryption patterns, and system interactions. Random undersampling is a mechanism designed to address class imbalance in datasets, particularly in organizing tasks. It consists of reducing The total count in the majority category to equalize the class distribution. The proposed model demonstrates superior performance in early-stage ransomware detection with improved false-positive rates and reduced computational overhead. Further research will focus on optimizing the QNN architecture and researching hybrid models that harmonize conventional and quantum techniques. The goal is to amplify the scalability and productivity of ransomware detection structures in dynamic threat landscapes.