@inproceedings{5103b3f69c5f489c8c6e96475c909f82,
title = "Towards a data semantics management system for internet traffic",
abstract = "Although current Internet operations generate voluminous data, they remain largely oblivious of traffic data semantics. This poses many inefficiencies and challenges due to emergent or anomalous behavior impacting the vast array of Internet elements such as services and protocols. In this paper, we propose a Data Semantics Management System (DSMS) for learning Internet traffic data semantics to enable smarter semantics-driven networking operations. We extract networking semantics and build and utilize a dynamic ontology of network concepts to better recognize and act upon emergent or abnormal behavior. Our DSMS utilizes: (1) Latent Dirichlet Allocation algorithm (LDA) for latent features extraction and semantics reasoning; (2) big tables as a cloud-like data storage technique to maintain large-scale data; and (3) Locality Sensitive Hashing algorithm (LSH) for reducing data dimensionality. Our preliminary evaluation using real Internet traffic shows the efficacy of DSMS for learning behavior of normal and abnormal traffic data and for accurately detecting anomalies at low cost.",
keywords = "Behavior Analysis Analysis, Big Data, Data Reduction, Network Semantics, Semantic Reasoning",
author = "Bassem Mokhtar and Mohamed Eltoweissy",
year = "2014",
doi = "10.1109/NTMS.2014.6814054",
language = "English",
isbn = "9781479932238",
series = "2014 6th International Conference on New Technologies, Mobility and Security - Proceedings of NTMS 2014 Conference and Workshops",
publisher = "IEEE Computer Society",
booktitle = "2014 6th International Conference on New Technologies, Mobility and Security - Proceedings of NTMS 2014 Conference and Workshops",
note = "2014 6th International Conference on New Technologies, Mobility and Security, NTMS 2014 ; Conference date: 30-03-2014 Through 02-04-2014",
}