MTRAC - discovering M2M devices in cellular networks from coarse-grained measurements

Machine-to-Machine (M2M) network traffic is becoming highly relevant in nowadays cellular networks. The ever-increasing number of M2M devices is heavily modifying the traffic patterns observed in cellular networks, and the interest in discovering and tracking these devices is rapidly growing among operators. In this paper we introduce MTRAC, a complete approach for M2M TRAffic Classification, capable of discovering M2M devices from coarse-grained measurements. MTRAC uses different Machine Learning (ML) algorithms to unveil M2M devices in cellular networks. It relies on very simple traffic descriptors to characterize the communication patterns of each device. These descriptors are robust to traffic encryption techniques, and improve the portability of the MTRAC approach to other network scenarios. MTRAC is implemented on top of DBStream, a novel Data Stream Warehouse which allows to classify M2M devices in an on-line basis, using different temporal and logical traffic aggregations. We study the performance of MTRAC in the on-line classification of more than two months of traffic observed in a operational, nationwide cellular network, comparing different ML algorithms and different traffic aggregation techniques. To the best of our knowledge, MTRAC is the first ML-based approach for automatic M2M device classification in operational cellular networks.