Abstract
In every organization securing the data is more important from the cyber attacks, the traditional based malware detectionare less effective, Especiallywhen zero-day attacks hit the malware detection is not possible. This Generative Adversarial Network has come up the idea and to implement the security and prevent against the known and unknown malware, also the system implemented wit the Malware classifier Optimizier(MCOGAN), a GAN based malware classifier generates adversarial samples for refine classifiers and improve their robustness. The GAN-based optimization and adaptive learning framework helps in reducing the false positive and false negatives and performs in maintaining the high detection accuracy. The realtime monitoring ensures continuous protection and perform response to emerging threats. These evaluation on multiple datasets improves the performance, system's ability and long-term defense in cybersecurity against the malware strategies.