CFOL: A Superficial Machine Learning Methodology to Identify Malwares by Using Convolutional Fuzzy Optimization Logic

Malware poses a significant threat to the security and integrity of modern computer systems. Traditional malware detection methods, such as signature-based approaches, struggle to keep pace with the rapid evolution of malware. This paper presents Convolutional Fuzzy Optimization Logic (CFOL), a novel machine learning-based methodology for malware detection that leverages Convolutional Neural Networks (CNNs) enhanced with an Attention Mechanism. Data was collected from Kaggle's malware dataset, which was preprocessed to extract key features like file headers, API calls, and metadata. The CNN with Attention Mechanism demonstrated an accuracy of 97.80%, outperforming traditional models such as Random Forest and Support Vector Machines (SVM), which achieved accuracies of 91.3% and 89.6%, respectively. The Attention Mechanism allowed the model to prioritize the most critical malware features, significantly reducing the false positive rate (FPR) to 2.2%, one of the lowest among the tested models. Additionally, the system achieved high precision (98.2%), recall (97.1%), and F1 score (97.35%). The integration of fuzzy logic optimization further enhanced the decision-making process in cases of ambiguous classifications. The results validate the effectiveness of CFOL in detecting complex and evolving malware with high precision and minimal false positives.