A Comparative Study of Lightweight, Sparse Autoencoder-Based Classifiers for Edge Network Devices: An Efficiency Analysis of Feed-Forward and Deep Neural Networks.

This study proposes a lightweight classification framework for anomaly traffic detection in edge computing environments. Thirteen packet- and flow-level features extracted from the CIC-IDS2017 dataset were compressed into 4-dimensional latent vectors using a Sparse Autoencoder (SAE). Two classifiers were compared under the same pipeline: a Feed-Forward network (SAE-FF) and a Deep Neural Network (SAE-DNN). To ensure generalization, all experiments were conducted with 5-fold cross-validation. Performance evaluation revealed that SAE-DNN achieved superior classification performance, with an average accuracy of 99.33% and an AUC of 0.9993. The SAE-FF model, although exhibiting lower performance (average accuracy of 93.66% and AUC of 0.9758), maintained stable outcomes and offered significantly lower computational complexity (~40 FLOPs) compared with SAE-DNN (~8960 FLOPs). Device-level analysis confirmed that SAE-FF was the most efficient option for resource-constrained platforms such as Raspberry Pi 4, whereas SAE-DNN achieved real-time inference capability on the Coral Dev Board by leveraging Edge TPU acceleration. To quantify this trade-off between accuracy and efficiency, we introduce the Edge Performance Efficiency Score (EPES), a composite metric that integrates accuracy, latency, memory usage, FLOPs, and CPU performance into a single score. The proposed EPES provides a practical and comprehensive benchmark for balancing accuracy and efficiency and supporting device-specific model selection in practical edge deployments. These findings highlight the importance of system-aware evaluation and demonstrate that EPES can serve as a valuable guideline for efficient anomaly traffic classification in resource-limited environments.