Sparse-Clustered Network with Selective Decoding for Internet Traffic Classification

ISPs and network administrators use Internet Packet Classification (IPC) to categorize packets into flows (traffic sharing IP addresses, ports, and protocol), and thereby the application classes generating them. Distinguishing between safe and malicious traffic aids in network intrusion interception. Likewise, categorizing applications into classes is useful for traffic management for better service. Traditional IPC based on port numbers and payload pattern recognition are no longer effective because current applications can dynamically change port numbers and cipher their contents. Recent machine learning (ML) IPC solutions have speed-bounded accuracy, and complex implementation due to their dependence on packet sizes and order of arrival. We propose a new IPC approach that uses associative memory (AM) based on sparse-clustered network with selective decoding. Unlike ML approaches our solution takes bits extracted directly from the flow ID as input, which greatly reduces system complexity and cost. It achieves 85.1% accuracy, consumes only 516 Mbits of memory, and runs 387x faster than the state-of-the-art FPGA-implemented approach, which uses Support Vector Machines.