Avoidance of Malwares in Large Scale Network using Ensemble Deep Generative Adversarial Network

Artificial Intelligence (AI) has progressed considerably in recent years and now influences all parts of society and jobs. Al is beneficial to many fields, such as gaming. language processing, healthcare, production, education, and others. This tendency also affects the realm of cyber security, in which Al has been used in cyber space for both attack and defense. False alarms are a problem for end-users which disrupt business by delaying any essential response and generally damage efficiency. The fine-tuning process is a compromise between eliminating false alarms and maintaining the level of safety. In this paper, an Ensemble Deep Generative Adversarial Networks (EDGAN) is developing for the classification of threats in case of large network. The EDGAN is designed in such a way that it undergoes series of process to eliminate the threat in a novel way. The simulation is conducted to test the efficacy of the model and the results of simulation shows higher rate of security in classifying the instances than other methods.