Optimized Malware Detection Using Hybrid Federated‐APO Algorithm

Abstract

ABSTRACT The proliferation of unprotected Internet of Things (IoT) devices has been exponential in recent years, and it will continue to rise in the years to come owing to improvements in wireless connectivity. Due to its vulnerability to malware, reliable techniques for detecting IoT malware have become imperative. Problems with non‐independently and identically distributed data and poor generalizability nevertheless prevent us from reaching our objective. A methodical strategy for detecting malware is laid out in this study. Federated learning (FL) methods generate a notable degree of communication overhead given the high volumes of weights sent and received from the client‐side trained models. By combining the benefits of FL with Artificial Plant Optimization Algorithm (APO), this study intends to solve this problem. APO facilitated FL framework have been assessed applying it to benchmark malware datasets. In terms of effectiveness, reliability, scalability, generalizability, and communication efficiency, the APO facilitated FL has been experimentally evaluated on readily accessible malware datasets.