An Effective Feed Forward Neural Network Model for Detection of Malware

Malware attacks are a serious threat to computer systems, making malware detection an important field of cybersecurity research. Particularly when it comes to detecting novel and unidentified types of malware, traditional approaches to malware detection have limits in terms of accuracy and efficacy. This study introduces a cutting-edge advanced neural network model for malware detection, achieving over 99.96% accuracy on test data through meticulous optimization. Emphasizing feature engineering and model configuration using RMSprop optimizer, the research highlights their pivotal roles in enhancing performance. Further fine-tuning with Stochastic Gradient Descent ensures the model's resilience. This work not only contributes a formidable tool to the cybersecurity arsenal but also points towards future improvements, emphasizing data diversity, advanced feature engineering, and continuous model updates for effective threat combat in a rapidly changing landscape.