Toward Enhancing Malware Detection Using Practical Swarm Optimization in Honeypot

Abstract

Malware attacks have become a pressing concern in the domain of computer and communication systems, posing significant threats to data security and privacy. A practical approach is represented aiming to enhance the malware detection process with the help of Honeypot. A hybrid model of particle swarm optimization (PSO) integrated with Fuzzy_KNN algorithms is used in this research. Numerical simulations and mathematical analysis are conducted after developing numeric codes of this scheme. The performance and practicality are examined via these evaluation metrics including accuracy, precision, recall, and F1-score. Based on the numerical investigations, the findings confirmed satisfying performance measures of the hybrid model. The FuzzyKNN algorithm does attain the most remarkable effectiveness, achieving accuracy between 99.95% and 99.97%. This model employs a premium method of neighbourhood voting with an element of fuzziness and excels in large or complex datasets where patterns may emerge based on instance similarity.