Efficient and Lightweight Convolutional Networks for IoT Malware Detection: A Federated Learning Approach

Abstract

Over the past few years, billions of unsecured Internet of Things (IoT) devices have been produced and released, and that number will only grow as wireless technology advances. As a result of their susceptibility to malware, effective methods have become necessary for identifying IoT malware. However, the low generalizability and the nonindependently and identically distributed data (non-IID) still pose a major challenge to achieving this goal. In this work, a new federated malware detection paradigm, termed FED-MAL, is introduced to collaboratively train multiple distributed edge devices to detect malware. In FED-MAL, the malware binaries are transformed into an image format to lessen the impact on non-IID, and then a compact convolutional model, named AM-NET, is proposed to learn the malware patterns as an image recognition task. The compact nature of AM-NET makes it an appropriate choice for deployment on resource-constrained IoT devices. Following, a refined edge-based adversarial training is given in FED-MAL to empower generalizability and resistibility by generating adversarial samples from various participating clients. Experimental evaluation on publicly available malware data sets shows that the FED-MAL is efficacious, reliable, expandable, generalizable, and communication efficient.