Random Forest Based Malware Detection Using Java Microservices

Malware or malicious software have been a major cy-bersecurity threat since it holds the capability to harm the system once in contact leading to total destruction of confidentiality, in-tegrity and availability the system provides thus various mitigation and detection strategies have been developed over decades in order to counter this issue. In this paper, the proposal of a new model has been brought forward that uses microservices to im-plement random forest algorithm for malware detection and analysis in order to deal with complex dataset that leads to a high computational overhead and resource consumption. The latter part of this paper discusses the overall model and how it can lead to improved accuracy overtime when the size of dataset provided increases by thousands. In conclusion a comparison between the proposed model which implements the microservice and the baseline is shown which details how the accuracy can fluctuate when dealing with a larger dataset, the comparison also details the parameters such as AUC score, Fl score, precision & accu-racy ofmodels to showcase the difference in values between the proposed and baseline model.