Self-Organizing Neural Grove for Malware Detection in IoT Edge Devices

Deep learning models, particularly convolutional neural networks (CNNs), have demonstrated exceptional classification performance across various practical applications. However, their training time scales significantly with network depth, rendering them suboptimal for resource-constrained environments. As a practical alternative, multiple classifier systems (MCSs) based on self-generating neural trees provide faster training and lower computational overhead. In this study, we propose the Self-Organizing Neural Grove (SONG), an ensemble learning model featuring a novel pruning technique designed to optimize classification efficiency. We evaluate SONG's performance on a Raspberry Pi 3, a standard edge computing platform. Through comparative experiments against an unpruned MCS, a C4.5-based MCS, and the k-nearest neighbors (k-NN) algorithm, we demonstrate that SONG achieves superior classification accuracy while substantially reducing both computation time and memory footprint. These advantages are consistent across benchmark datasets and real-world cybersecurity tasks, underscoring the high suitability of SONG for edge computing applications.