Numeric Feature Analysis in Deep Learning-based Ransomware Detection with Convolution Neural Network Models - Ogundele, Lukman; Adepoju, Julius; AYO, Femi Emmanuel; Akano, Idayat Abike; Oyedemi, Oluyemisi Adenike (INFOCOMP Journal of Computer Science, 2025)
The research introduces ResMalNet, a convolutional neural network architecture designed for malware detection. The architecture employs domain expertise to identify critical behavioral categories, such as registry operations, network activities, and process/file interactions, and statistical optimization to select the most discriminative numeric features. ResMalNet outperforms four established CNN architectures, achieving 98.91% accuracy and 98.92% precision while maintaining balanced recall and F1-scores of 98.91%. The technical implementation addresses three persistent challenges in malware classification: prevention of model over-fitting, preservation of critical feature relationships, and optimization of residual block designs. Experimental results show architectural specialization through residual connections improves accuracy by 1.82% over conventional CNN designs, domain-informed feature selection reduces false positive rates by 42%, and exceptional detection rates for previously unseen malware variants during validation testing. The ResMalNet framework offers practical implementation guidelines for security systems, with immediate applications in next-generation endpoint protection solutions and network monitoring infrastructure.