AntDroidNet Cybersecurity Model: A Hybrid Integration of Ant Colony Optimization and Deep Neural Networks for Android Malware Detection

Malware detection is a vital problem, and efficient methods that can efficiently detect malware are needed. The increasing use of mobile computers makes malware detection a vital part of security in an era where smartphones have come to play a key role in many of our daily lives. Earlier approaches, however, suffer from high false positive rates; they are not scalable for larger databases, or they are not amenable to adapt well to novel zero-day malware. For these reasons, the demand for more sensitive and flexible detection models is high. In this study, we develop a hybrid mobile malware detection framework that leverages ant colony optimization (ACO) and deep neural networks (DNNs) to improve detection accuracy, reduce the rate of false positives, and make the model resilient to new malware. AntDroidNet is a novel ACO-enabled feature selection model that dynamically reduces the feature dimensionality by selecting single instances to include the most informative properties and avoid dimensionality. A DNN is consequently constructed to train the determined set of features, improving the identified classification performance and decreasing the number of instances with false discove