Abstract: Packet classification has become one of the most important application techniques in network security since the last decade. The technique involves a traffic descriptor or userdefined criteria to categorize packets to a specific forwarding class which will be accessible for future security handling. In this chapter, we present two new schemes, Hierarchical Cross-Producting and Controlled Cross-producting, to achieve fast packet classification. The first scheme simplifies the classification procedure and decreases the distinct combinations of fields by hierarchically decomposing the multi-dimensional space based on the concept of telescopic search. Analogous to the use of telescopes with different powers, which is defined as the degree to which a telescope multiplies the apparent diameter of an object in optical terms, a multiplestep process is used to search for targets. In this scheme, the multi-dimensional space is endowed with a hierarchical property which self-divides into several smaller subspaces, whereas the procedure of packet classification is translated into recursive searching for matching subspaces. The required storage of our scheme could be significantly reduced since the distinct field specifications of subspaces are manageable. Next, we combine the technique of cross-producting with linear search to make packet classification both fast and scalable. The new algorithm, Controlled Cross-producting, could improve the scalability of cross-producting significantly with respect to storage, while maintaining the search latency. In addition, we introduce several refinements and procedures for incremental update. The performance of both algorithms is evaluated based on both real and synthetic filter databases. The experimental results demonstrate the effectiveness and scalability of both schemes.