Hybrid intelligence for semantics-enhanced networking operations

Endowing the semantically-oblivious Internet with Intelligence would advance the Internet capability to learn traffic behavior and to predict future events. In this paper, we propose a hybrid intelligence memory system, or NetMem, for network-semantics reasoning and targeting Internet intelligence. NetMem provides a memory structure, mimicking the human memory functionalities, via short-term memory (StM) and long-term memory (LtM). NetMem has the capability to build runtime accessible dynamic network-concept ontology (DNCO) at different levels of granularity. We integrate Latent Dirichlet Allocation (LDA) and Hidden Markov Models (HMM) to extract network-semantics based on learning patterns and recognizing features with syntax and semantic dependencies. Due to the large scale and high-dimensionality of Internet data, we utilize the Locality Sensitive Hashing (LSH) algorithm for data dimensionality reduction. Simulation results using real network traffic show that NetMem with hybrid intelligence learn traffic data semantics effectively and efficiently even with significant reduction in volume and dimensionality of data, thus enhancing Internet intelligence for self-/situation-awareness and event/behavior prediction.